idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
|---|---|---|---|---|---|---|
38,201 | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I have read that "When you get more and more data, you can find
statistically significant differences wherever you look"
This is not always true, however if your null hypothesis is that two groups of people are exactly 100% the same then it is, because that null hypothesis will almost always or always be false. Inst... | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I have read that "When you get more and more data, you can find
statistically significant differences wherever you look"
This is not always true, however if your null hypothesis is that two groups | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I have read that "When you get more and more data, you can find
statistically significant differences wherever you look"
This is not always true, however if your null hypothesis is that two groups of people are exactly 100% ... | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I have read that "When you get more and more data, you can find
statistically significant differences wherever you look"
This is not always true, however if your null hypothesis is that two groups |
38,202 | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I also wish to highlight that you will not always find statistically significant results, even with nearly infinite data. Statistically significant results only represent what are likely to be true differences, regardless of size. If this difference does not exist, then the number of cases does not matter. Consider two... | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I also wish to highlight that you will not always find statistically significant results, even with nearly infinite data. Statistically significant results only represent what are likely to be true di | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I also wish to highlight that you will not always find statistically significant results, even with nearly infinite data. Statistically significant results only represent what are likely to be true differences, regardless of si... | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I also wish to highlight that you will not always find statistically significant results, even with nearly infinite data. Statistically significant results only represent what are likely to be true di |
38,203 | Why does statistical significance increase with data, BUT the effects may not be meaningful? | It is not necessarily the case that you will always find significant differences as you increase sample size, but it becomes more and more likely. As several people have pointed out, truly identical samples may not result in a significant difference. What it does do is make very, very small differences much more likely... | Why does statistical significance increase with data, BUT the effects may not be meaningful? | It is not necessarily the case that you will always find significant differences as you increase sample size, but it becomes more and more likely. As several people have pointed out, truly identical s | Why does statistical significance increase with data, BUT the effects may not be meaningful?
It is not necessarily the case that you will always find significant differences as you increase sample size, but it becomes more and more likely. As several people have pointed out, truly identical samples may not result in a ... | Why does statistical significance increase with data, BUT the effects may not be meaningful?
It is not necessarily the case that you will always find significant differences as you increase sample size, but it becomes more and more likely. As several people have pointed out, truly identical s |
38,204 | Why does statistical significance increase with data, BUT the effects may not be meaningful? | Assume you have a treatment for the common cold which may or may not work. You administer it to one person, and that person gets better. It could be that your treatment is working, or it could be that the person just happened to get better by chance.
Now if you apply this treatment to two people and they both get bette... | Why does statistical significance increase with data, BUT the effects may not be meaningful? | Assume you have a treatment for the common cold which may or may not work. You administer it to one person, and that person gets better. It could be that your treatment is working, or it could be that | Why does statistical significance increase with data, BUT the effects may not be meaningful?
Assume you have a treatment for the common cold which may or may not work. You administer it to one person, and that person gets better. It could be that your treatment is working, or it could be that the person just happened t... | Why does statistical significance increase with data, BUT the effects may not be meaningful?
Assume you have a treatment for the common cold which may or may not work. You administer it to one person, and that person gets better. It could be that your treatment is working, or it could be that |
38,205 | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I believe user27840 has the right answer, but doesn't quite nail the intuition...
Let's take a common case: you are comparing the means of two groups, and your null hypothesis is that they are (exactly) equal. The test is also making an assumption about the distribution of the data, often that the data has a "normal di... | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I believe user27840 has the right answer, but doesn't quite nail the intuition...
Let's take a common case: you are comparing the means of two groups, and your null hypothesis is that they are (exactl | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I believe user27840 has the right answer, but doesn't quite nail the intuition...
Let's take a common case: you are comparing the means of two groups, and your null hypothesis is that they are (exactly) equal. The test is also ... | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I believe user27840 has the right answer, but doesn't quite nail the intuition...
Let's take a common case: you are comparing the means of two groups, and your null hypothesis is that they are (exactl |
38,206 | Why does statistical significance increase with data, BUT the effects may not be meaningful? | The traditional hypothesis testing framework seeks to keep type I error constant. The likelihood paradigm could be said to be more meaningful in this regard, as both type I and type II errors $\rightarrow 0$ as $n \rightarrow \infty$. This makes pure likelihood-based (as opposed to sample-space-based frequentist meth... | Why does statistical significance increase with data, BUT the effects may not be meaningful? | The traditional hypothesis testing framework seeks to keep type I error constant. The likelihood paradigm could be said to be more meaningful in this regard, as both type I and type II errors $\right | Why does statistical significance increase with data, BUT the effects may not be meaningful?
The traditional hypothesis testing framework seeks to keep type I error constant. The likelihood paradigm could be said to be more meaningful in this regard, as both type I and type II errors $\rightarrow 0$ as $n \rightarrow ... | Why does statistical significance increase with data, BUT the effects may not be meaningful?
The traditional hypothesis testing framework seeks to keep type I error constant. The likelihood paradigm could be said to be more meaningful in this regard, as both type I and type II errors $\right |
38,207 | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I think this happens because people analyse their data after it has been observed. Further this analysis is not done "just for the sake of it" - you may change your mind about what is important based on this analysis (as you should).
As a simple example, the comparison of the means of two groups - say jurisdiction A h... | Why does statistical significance increase with data, BUT the effects may not be meaningful? | I think this happens because people analyse their data after it has been observed. Further this analysis is not done "just for the sake of it" - you may change your mind about what is important based | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I think this happens because people analyse their data after it has been observed. Further this analysis is not done "just for the sake of it" - you may change your mind about what is important based on this analysis (as you s... | Why does statistical significance increase with data, BUT the effects may not be meaningful?
I think this happens because people analyse their data after it has been observed. Further this analysis is not done "just for the sake of it" - you may change your mind about what is important based |
38,208 | Effect size and statistical significance | Yes, this may completely make sense. In fact, it is also possible (perhaps rarer) to see a large estimated effect size without there being statistically significant evidence it isn't zero.
The issue is that your effect size is just a point estimate and hence is a random variable that depends on the particular sample... | Effect size and statistical significance | Yes, this may completely make sense. In fact, it is also possible (perhaps rarer) to see a large estimated effect size without there being statistically significant evidence it isn't zero.
The issu | Effect size and statistical significance
Yes, this may completely make sense. In fact, it is also possible (perhaps rarer) to see a large estimated effect size without there being statistically significant evidence it isn't zero.
The issue is that your effect size is just a point estimate and hence is a random varia... | Effect size and statistical significance
Yes, this may completely make sense. In fact, it is also possible (perhaps rarer) to see a large estimated effect size without there being statistically significant evidence it isn't zero.
The issu |
38,209 | Effect size and statistical significance | Yes. This basically means that you see a medium (say) effect, but you can't be sure at the 95% level that what you see is not due to a random fluctuation. This probably happens because your sample is too small. You want to have a look at http://en.wikipedia.org/wiki/Statistical_power
(And to all the professionals here:... | Effect size and statistical significance | Yes. This basically means that you see a medium (say) effect, but you can't be sure at the 95% level that what you see is not due to a random fluctuation. This probably happens because your sample is | Effect size and statistical significance
Yes. This basically means that you see a medium (say) effect, but you can't be sure at the 95% level that what you see is not due to a random fluctuation. This probably happens because your sample is too small. You want to have a look at http://en.wikipedia.org/wiki/Statistical_... | Effect size and statistical significance
Yes. This basically means that you see a medium (say) effect, but you can't be sure at the 95% level that what you see is not due to a random fluctuation. This probably happens because your sample is |
38,210 | Effect size and statistical significance | What a p-value is:
A p-value answers this question: If, in the population from which this sample is drawn, there was really no effect at all, how likely is a result as extreme or more extreme than the one we got in this sample?
That is all it means.
This question is almost never of interest
Effect sizes (like Cohen's ... | Effect size and statistical significance | What a p-value is:
A p-value answers this question: If, in the population from which this sample is drawn, there was really no effect at all, how likely is a result as extreme or more extreme than the | Effect size and statistical significance
What a p-value is:
A p-value answers this question: If, in the population from which this sample is drawn, there was really no effect at all, how likely is a result as extreme or more extreme than the one we got in this sample?
That is all it means.
This question is almost neve... | Effect size and statistical significance
What a p-value is:
A p-value answers this question: If, in the population from which this sample is drawn, there was really no effect at all, how likely is a result as extreme or more extreme than the |
38,211 | Should a multiple choice poll contain a neutral response? | Neutral points can mean many different things to many people. The way you labeled the middle choice yourself reflects this uncertainty. Some reasons for choosing the neutral point from the perspective of a participant:
I don't care to really think about my answer to this question (I just want to get paid and leave)
I ... | Should a multiple choice poll contain a neutral response? | Neutral points can mean many different things to many people. The way you labeled the middle choice yourself reflects this uncertainty. Some reasons for choosing the neutral point from the perspective | Should a multiple choice poll contain a neutral response?
Neutral points can mean many different things to many people. The way you labeled the middle choice yourself reflects this uncertainty. Some reasons for choosing the neutral point from the perspective of a participant:
I don't care to really think about my answ... | Should a multiple choice poll contain a neutral response?
Neutral points can mean many different things to many people. The way you labeled the middle choice yourself reflects this uncertainty. Some reasons for choosing the neutral point from the perspective |
38,212 | Should a multiple choice poll contain a neutral response? | I think this whole "force people to choose" thing is just a complete red herring. People say it to me all the time. To me it sounds like "force people to state the capital of Uzbekistan". They don't know, and forcing them won't make them know any better.
With that mini-rant over, my only sensible contribution is to say... | Should a multiple choice poll contain a neutral response? | I think this whole "force people to choose" thing is just a complete red herring. People say it to me all the time. To me it sounds like "force people to state the capital of Uzbekistan". They don't k | Should a multiple choice poll contain a neutral response?
I think this whole "force people to choose" thing is just a complete red herring. People say it to me all the time. To me it sounds like "force people to state the capital of Uzbekistan". They don't know, and forcing them won't make them know any better.
With th... | Should a multiple choice poll contain a neutral response?
I think this whole "force people to choose" thing is just a complete red herring. People say it to me all the time. To me it sounds like "force people to state the capital of Uzbekistan". They don't k |
38,213 | Should a multiple choice poll contain a neutral response? | I try to avoid questions with more than two answers, as it is impossible to compare them between users. (good vs. very good can be very subjective).
I rephrase most questions into binary type (giving though the possibility to be indiffernt):
"Would you use the product everyday?"
Yes No Indifferent
"Would you recommend ... | Should a multiple choice poll contain a neutral response? | I try to avoid questions with more than two answers, as it is impossible to compare them between users. (good vs. very good can be very subjective).
I rephrase most questions into binary type (giving | Should a multiple choice poll contain a neutral response?
I try to avoid questions with more than two answers, as it is impossible to compare them between users. (good vs. very good can be very subjective).
I rephrase most questions into binary type (giving though the possibility to be indiffernt):
"Would you use the p... | Should a multiple choice poll contain a neutral response?
I try to avoid questions with more than two answers, as it is impossible to compare them between users. (good vs. very good can be very subjective).
I rephrase most questions into binary type (giving |
38,214 | Should a multiple choice poll contain a neutral response? | I would suggest that when trying to judge people's like or dislike of something, there are a few relevant scales of measure:
How strong are any positive feelings for the product.
How strong are any negative feelings for the product.
How thoroughly have things that would cause positive or negative feelings been explore... | Should a multiple choice poll contain a neutral response? | I would suggest that when trying to judge people's like or dislike of something, there are a few relevant scales of measure:
How strong are any positive feelings for the product.
How strong are any n | Should a multiple choice poll contain a neutral response?
I would suggest that when trying to judge people's like or dislike of something, there are a few relevant scales of measure:
How strong are any positive feelings for the product.
How strong are any negative feelings for the product.
How thoroughly have things t... | Should a multiple choice poll contain a neutral response?
I would suggest that when trying to judge people's like or dislike of something, there are a few relevant scales of measure:
How strong are any positive feelings for the product.
How strong are any n |
38,215 | Should a multiple choice poll contain a neutral response? | If you wish to detect overt opinions then put in a neutral option. If you wish to detect any potential positive or negative bias then leave it out. As caracal said, label things as unambiguously as possible with respect to what you wish the options to reflect.
I've seen studies where only the form of response was cha... | Should a multiple choice poll contain a neutral response? | If you wish to detect overt opinions then put in a neutral option. If you wish to detect any potential positive or negative bias then leave it out. As caracal said, label things as unambiguously as | Should a multiple choice poll contain a neutral response?
If you wish to detect overt opinions then put in a neutral option. If you wish to detect any potential positive or negative bias then leave it out. As caracal said, label things as unambiguously as possible with respect to what you wish the options to reflect.... | Should a multiple choice poll contain a neutral response?
If you wish to detect overt opinions then put in a neutral option. If you wish to detect any potential positive or negative bias then leave it out. As caracal said, label things as unambiguously as |
38,216 | Should a multiple choice poll contain a neutral response? | The fact that you are forcing the respondent for a positive answer or negative answer, in this situation, is not correct; the respondent may be undecided and it is a possibility; it is more when it is a new product. If you are developing an instrument to measure quality it is better that you use 5 options as given by y... | Should a multiple choice poll contain a neutral response? | The fact that you are forcing the respondent for a positive answer or negative answer, in this situation, is not correct; the respondent may be undecided and it is a possibility; it is more when it is | Should a multiple choice poll contain a neutral response?
The fact that you are forcing the respondent for a positive answer or negative answer, in this situation, is not correct; the respondent may be undecided and it is a possibility; it is more when it is a new product. If you are developing an instrument to measure... | Should a multiple choice poll contain a neutral response?
The fact that you are forcing the respondent for a positive answer or negative answer, in this situation, is not correct; the respondent may be undecided and it is a possibility; it is more when it is |
38,217 | Expected value of random variable that is defined by another | Your random variable takes the value $1$ with probability $p+\frac{1-p}{k}$, and takes each value $j\in\{2, \dots, k\}$ with probability $\frac{1-p}{k}$. So the expectation is simply
$$ \begin{align*} EX
= & 1\times \big(p+\frac{1-p}{k}\big) + \sum_{j=2}^k j\times\frac{1-p}{k} \\
= & p+\frac{1-p}{k}\times \sum_{j=1}^k... | Expected value of random variable that is defined by another | Your random variable takes the value $1$ with probability $p+\frac{1-p}{k}$, and takes each value $j\in\{2, \dots, k\}$ with probability $\frac{1-p}{k}$. So the expectation is simply
$$ \begin{align*} | Expected value of random variable that is defined by another
Your random variable takes the value $1$ with probability $p+\frac{1-p}{k}$, and takes each value $j\in\{2, \dots, k\}$ with probability $\frac{1-p}{k}$. So the expectation is simply
$$ \begin{align*} EX
= & 1\times \big(p+\frac{1-p}{k}\big) + \sum_{j=2}^k j\... | Expected value of random variable that is defined by another
Your random variable takes the value $1$ with probability $p+\frac{1-p}{k}$, and takes each value $j\in\{2, \dots, k\}$ with probability $\frac{1-p}{k}$. So the expectation is simply
$$ \begin{align*} |
38,218 | Expected value of random variable that is defined by another | Your answer is okay and is justified below.
You can write:$$X=B+(1-B)Y$$where $B\sim\text{Bernoulli}(p)$ and $Y\sim\text{Unif}(k)$ are independent random variables.
Then:$$\mathbb EX=\mathbb EB+\mathbb E[(1-B)Y]=\mathbb EB+\mathbb E(1-B)\mathbb EY=p+(1-p)\left(\frac12+\frac12k\right)$$
This way of working is recommend... | Expected value of random variable that is defined by another | Your answer is okay and is justified below.
You can write:$$X=B+(1-B)Y$$where $B\sim\text{Bernoulli}(p)$ and $Y\sim\text{Unif}(k)$ are independent random variables.
Then:$$\mathbb EX=\mathbb EB+\math | Expected value of random variable that is defined by another
Your answer is okay and is justified below.
You can write:$$X=B+(1-B)Y$$where $B\sim\text{Bernoulli}(p)$ and $Y\sim\text{Unif}(k)$ are independent random variables.
Then:$$\mathbb EX=\mathbb EB+\mathbb E[(1-B)Y]=\mathbb EB+\mathbb E(1-B)\mathbb EY=p+(1-p)\le... | Expected value of random variable that is defined by another
Your answer is okay and is justified below.
You can write:$$X=B+(1-B)Y$$where $B\sim\text{Bernoulli}(p)$ and $Y\sim\text{Unif}(k)$ are independent random variables.
Then:$$\mathbb EX=\mathbb EB+\math |
38,219 | Lasso coefficient for some features is higher than Linear Regression Coefficient | As German Demidov notes, this is perfectly fine. The Lasso will shrink some of your coefficients to zero, but it does not have the property of shrinking all coefficients compared to the OLS estimate. Rather, it may increase some coefficients to "compensate" for the ones it has shrunk. There is nothing to worry about. (... | Lasso coefficient for some features is higher than Linear Regression Coefficient | As German Demidov notes, this is perfectly fine. The Lasso will shrink some of your coefficients to zero, but it does not have the property of shrinking all coefficients compared to the OLS estimate. | Lasso coefficient for some features is higher than Linear Regression Coefficient
As German Demidov notes, this is perfectly fine. The Lasso will shrink some of your coefficients to zero, but it does not have the property of shrinking all coefficients compared to the OLS estimate. Rather, it may increase some coefficien... | Lasso coefficient for some features is higher than Linear Regression Coefficient
As German Demidov notes, this is perfectly fine. The Lasso will shrink some of your coefficients to zero, but it does not have the property of shrinking all coefficients compared to the OLS estimate. |
38,220 | Lasso coefficient for some features is higher than Linear Regression Coefficient | Lasso coefficients can shrink again while you get closer to the OLS solution.
See for instance: Why under joint least squares direction is it possible for some coefficients to decrease in LARS regression?
Here is an image of the relationship between the coefficients and the error. The lasso balances the error (depicted... | Lasso coefficient for some features is higher than Linear Regression Coefficient | Lasso coefficients can shrink again while you get closer to the OLS solution.
See for instance: Why under joint least squares direction is it possible for some coefficients to decrease in LARS regress | Lasso coefficient for some features is higher than Linear Regression Coefficient
Lasso coefficients can shrink again while you get closer to the OLS solution.
See for instance: Why under joint least squares direction is it possible for some coefficients to decrease in LARS regression?
Here is an image of the relationsh... | Lasso coefficient for some features is higher than Linear Regression Coefficient
Lasso coefficients can shrink again while you get closer to the OLS solution.
See for instance: Why under joint least squares direction is it possible for some coefficients to decrease in LARS regress |
38,221 | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate quality? | This reminds me strongly of Sturgeon's Law:
90% of everything is crud.
The "law" was coined by scifi author and critic Theodore Sturgeon as a response to people being dismissive of scifi because "90% of it is crud". He realized that the same was true of any genre, but that (say) cozy mystery fans knew how to spot wor... | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate | This reminds me strongly of Sturgeon's Law:
90% of everything is crud.
The "law" was coined by scifi author and critic Theodore Sturgeon as a response to people being dismissive of scifi because "90 | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate quality?
This reminds me strongly of Sturgeon's Law:
90% of everything is crud.
The "law" was coined by scifi author and critic Theodore Sturgeon as a response to people being dismissive of scifi because "90% of it is ... | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate
This reminds me strongly of Sturgeon's Law:
90% of everything is crud.
The "law" was coined by scifi author and critic Theodore Sturgeon as a response to people being dismissive of scifi because "90 |
38,222 | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate quality? | Berkson's Paradox doesn't prove that there's no correlation between quality and popularity, and Ellenberg isn't claiming that it does - it just counters the argument that "there is a negative correlation between quality and popularity among novels I have heard of, therefore quality and popularity are negatively correla... | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate | Berkson's Paradox doesn't prove that there's no correlation between quality and popularity, and Ellenberg isn't claiming that it does - it just counters the argument that "there is a negative correlat | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate quality?
Berkson's Paradox doesn't prove that there's no correlation between quality and popularity, and Ellenberg isn't claiming that it does - it just counters the argument that "there is a negative correlation between... | Under Berkson's Fallacy, why can't popular novels be terrible because the masses fail to appreciate
Berkson's Paradox doesn't prove that there's no correlation between quality and popularity, and Ellenberg isn't claiming that it does - it just counters the argument that "there is a negative correlat |
38,223 | Why is the intercept in multiple regression changing when including/excluding regressors? | In addition to @DaveT's helpful answer, here are a few more clarifications regarding the estimated intercepts in your models.
Model 1
The (true) intercept in your first model
lm(mpg ~ 1, data=mtcars)
represents the mean value of mpg for all cars represented by the ones included in this data set, regardless of their d... | Why is the intercept in multiple regression changing when including/excluding regressors? | In addition to @DaveT's helpful answer, here are a few more clarifications regarding the estimated intercepts in your models.
Model 1
The (true) intercept in your first model
lm(mpg ~ 1, data=mtcars) | Why is the intercept in multiple regression changing when including/excluding regressors?
In addition to @DaveT's helpful answer, here are a few more clarifications regarding the estimated intercepts in your models.
Model 1
The (true) intercept in your first model
lm(mpg ~ 1, data=mtcars)
represents the mean value of... | Why is the intercept in multiple regression changing when including/excluding regressors?
In addition to @DaveT's helpful answer, here are a few more clarifications regarding the estimated intercepts in your models.
Model 1
The (true) intercept in your first model
lm(mpg ~ 1, data=mtcars) |
38,224 | Why is the intercept in multiple regression changing when including/excluding regressors? | Your professor comments concerning the conditional mean is when x meets a particular condition. In this case the intercept is the conditional mean of y when x=0. If x never takes the value of 0, then there is no conditional mean for x=0.
As a simple example let us look at y=(-x+10) for x from 0 to 10. If we fit mode... | Why is the intercept in multiple regression changing when including/excluding regressors? | Your professor comments concerning the conditional mean is when x meets a particular condition. In this case the intercept is the conditional mean of y when x=0. If x never takes the value of 0, then | Why is the intercept in multiple regression changing when including/excluding regressors?
Your professor comments concerning the conditional mean is when x meets a particular condition. In this case the intercept is the conditional mean of y when x=0. If x never takes the value of 0, then there is no conditional mean ... | Why is the intercept in multiple regression changing when including/excluding regressors?
Your professor comments concerning the conditional mean is when x meets a particular condition. In this case the intercept is the conditional mean of y when x=0. If x never takes the value of 0, then |
38,225 | Why is the intercept in multiple regression changing when including/excluding regressors? | Question part 1
The constant/intercept is defined as the mean of the dependent variable when you set all of the independent variables in your model to zero.
In short: The intercept term relates to the prediction based on the fitted model, when all independent variables are set to zero. This prediction may be more or ... | Why is the intercept in multiple regression changing when including/excluding regressors? | Question part 1
The constant/intercept is defined as the mean of the dependent variable when you set all of the independent variables in your model to zero.
In short: The intercept term relates to t | Why is the intercept in multiple regression changing when including/excluding regressors?
Question part 1
The constant/intercept is defined as the mean of the dependent variable when you set all of the independent variables in your model to zero.
In short: The intercept term relates to the prediction based on the fit... | Why is the intercept in multiple regression changing when including/excluding regressors?
Question part 1
The constant/intercept is defined as the mean of the dependent variable when you set all of the independent variables in your model to zero.
In short: The intercept term relates to t |
38,226 | How to visualize an evolution of a distribution in time? | EDIT
Didn't see it was a python question. I think the idea stands, but not sure if python implementation readily available.
You might want to look into so called ridge-plots using R package ggridges (and gganimate for animation).
Below is an example (obviously doesn't have to be an animation):
Code is available in... | How to visualize an evolution of a distribution in time? | EDIT
Didn't see it was a python question. I think the idea stands, but not sure if python implementation readily available.
You might want to look into so called ridge-plots using R package ggridges | How to visualize an evolution of a distribution in time?
EDIT
Didn't see it was a python question. I think the idea stands, but not sure if python implementation readily available.
You might want to look into so called ridge-plots using R package ggridges (and gganimate for animation).
Below is an example (obviously... | How to visualize an evolution of a distribution in time?
EDIT
Didn't see it was a python question. I think the idea stands, but not sure if python implementation readily available.
You might want to look into so called ridge-plots using R package ggridges |
38,227 | How to visualize an evolution of a distribution in time? | Always think about the nature of your situation: Who will be the audience for this plot? What are my goals for it? What are my data?
You state that you have a "distribution which depends on a parameter which evolves over time". If your audience is fairly sophisticated, and this is a known, studied distribution (e.... | How to visualize an evolution of a distribution in time? | Always think about the nature of your situation: Who will be the audience for this plot? What are my goals for it? What are my data?
You state that you have a "distribution which depends on a para | How to visualize an evolution of a distribution in time?
Always think about the nature of your situation: Who will be the audience for this plot? What are my goals for it? What are my data?
You state that you have a "distribution which depends on a parameter which evolves over time". If your audience is fairly sop... | How to visualize an evolution of a distribution in time?
Always think about the nature of your situation: Who will be the audience for this plot? What are my goals for it? What are my data?
You state that you have a "distribution which depends on a para |
38,228 | How to visualize an evolution of a distribution in time? | Ridge plots can be done with Python Seaborn.
I found this beautiful example in their documentation and will repost it here.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
# Create random data
rs = np.random.... | How to visualize an evolution of a distribution in time? | Ridge plots can be done with Python Seaborn.
I found this beautiful example in their documentation and will repost it here.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotl | How to visualize an evolution of a distribution in time?
Ridge plots can be done with Python Seaborn.
I found this beautiful example in their documentation and will repost it here.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white", rc={"axes.facecol... | How to visualize an evolution of a distribution in time?
Ridge plots can be done with Python Seaborn.
I found this beautiful example in their documentation and will repost it here.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotl |
38,229 | How to visualize an evolution of a distribution in time? | Assuming you have an empirical distribution for each day, as for example a store looking at total payment by each customer, per day. You can look upon this as a time series of histograms, and that could be plotted in various ways, maybe by a series of boxplots. If you have some example data we could try various options... | How to visualize an evolution of a distribution in time? | Assuming you have an empirical distribution for each day, as for example a store looking at total payment by each customer, per day. You can look upon this as a time series of histograms, and that cou | How to visualize an evolution of a distribution in time?
Assuming you have an empirical distribution for each day, as for example a store looking at total payment by each customer, per day. You can look upon this as a time series of histograms, and that could be plotted in various ways, maybe by a series of boxplots. I... | How to visualize an evolution of a distribution in time?
Assuming you have an empirical distribution for each day, as for example a store looking at total payment by each customer, per day. You can look upon this as a time series of histograms, and that cou |
38,230 | If I can make up priors, why can't I make up posteriors? | Well, in Bayesian statistics, you don't just "make up" your priors. You should be building a prior that best captures your knowledge before seeing the data. Otherwise, why anyone should care about the output of your Bayesian analysis is very hard to justify.
So while it's true that the practitioner has some sense of f... | If I can make up priors, why can't I make up posteriors? | Well, in Bayesian statistics, you don't just "make up" your priors. You should be building a prior that best captures your knowledge before seeing the data. Otherwise, why anyone should care about the | If I can make up priors, why can't I make up posteriors?
Well, in Bayesian statistics, you don't just "make up" your priors. You should be building a prior that best captures your knowledge before seeing the data. Otherwise, why anyone should care about the output of your Bayesian analysis is very hard to justify.
So ... | If I can make up priors, why can't I make up posteriors?
Well, in Bayesian statistics, you don't just "make up" your priors. You should be building a prior that best captures your knowledge before seeing the data. Otherwise, why anyone should care about the |
38,231 | If I can make up priors, why can't I make up posteriors? | If you have a belief about the distribution of your data after seeing data, then why would you be estimating its parameters with data? You already have the parameters. | If I can make up priors, why can't I make up posteriors? | If you have a belief about the distribution of your data after seeing data, then why would you be estimating its parameters with data? You already have the parameters. | If I can make up priors, why can't I make up posteriors?
If you have a belief about the distribution of your data after seeing data, then why would you be estimating its parameters with data? You already have the parameters. | If I can make up priors, why can't I make up posteriors?
If you have a belief about the distribution of your data after seeing data, then why would you be estimating its parameters with data? You already have the parameters. |
38,232 | If I can make up priors, why can't I make up posteriors? | In case of many problems in statistics you have some data, let's denote it as $X$, and want to learn about some "parameter" $\theta$ of the distribution of the data, i.e. calculate the $\theta|X$ kind of things (conditional distribution, conditional expectation etc.). There are several ways how can this be achieved, in... | If I can make up priors, why can't I make up posteriors? | In case of many problems in statistics you have some data, let's denote it as $X$, and want to learn about some "parameter" $\theta$ of the distribution of the data, i.e. calculate the $\theta|X$ kind | If I can make up priors, why can't I make up posteriors?
In case of many problems in statistics you have some data, let's denote it as $X$, and want to learn about some "parameter" $\theta$ of the distribution of the data, i.e. calculate the $\theta|X$ kind of things (conditional distribution, conditional expectation e... | If I can make up priors, why can't I make up posteriors?
In case of many problems in statistics you have some data, let's denote it as $X$, and want to learn about some "parameter" $\theta$ of the distribution of the data, i.e. calculate the $\theta|X$ kind |
38,233 | If I can make up priors, why can't I make up posteriors? | Philosophically, there is nothing wrong with “eliciting a posterior.” It’s a bit more difficult to do in a coherent manner than with priors (because you need to respect the likelihood), but IMO you are asking a really good question.
To turn this into something practical, “making up” a posterior is a potentially useful ... | If I can make up priors, why can't I make up posteriors? | Philosophically, there is nothing wrong with “eliciting a posterior.” It’s a bit more difficult to do in a coherent manner than with priors (because you need to respect the likelihood), but IMO you ar | If I can make up priors, why can't I make up posteriors?
Philosophically, there is nothing wrong with “eliciting a posterior.” It’s a bit more difficult to do in a coherent manner than with priors (because you need to respect the likelihood), but IMO you are asking a really good question.
To turn this into something pr... | If I can make up priors, why can't I make up posteriors?
Philosophically, there is nothing wrong with “eliciting a posterior.” It’s a bit more difficult to do in a coherent manner than with priors (because you need to respect the likelihood), but IMO you ar |
38,234 | Completely different results from lme() and lmer() | As it was noted in this answer, and also mentioned in one of the comments, the problem seems to be a local maximum. To see this more clearly I have written below a simple code to calculate the negative log-likelihood of this model and do the optimization using optim(). Starting with different initial values leads to th... | Completely different results from lme() and lmer() | As it was noted in this answer, and also mentioned in one of the comments, the problem seems to be a local maximum. To see this more clearly I have written below a simple code to calculate the negativ | Completely different results from lme() and lmer()
As it was noted in this answer, and also mentioned in one of the comments, the problem seems to be a local maximum. To see this more clearly I have written below a simple code to calculate the negative log-likelihood of this model and do the optimization using optim().... | Completely different results from lme() and lmer()
As it was noted in this answer, and also mentioned in one of the comments, the problem seems to be a local maximum. To see this more clearly I have written below a simple code to calculate the negativ |
38,235 | Completely different results from lme() and lmer() | I agree with @DimitrisRizopoulos's answer, and have a few more points to make.
I will start by saying that I am unhappy that lmer doesn't find the best answer - even though I suspect this situation is probably limited to small, unusual (see below) data sets. One of the reasons that lme may do better is that it fits on... | Completely different results from lme() and lmer() | I agree with @DimitrisRizopoulos's answer, and have a few more points to make.
I will start by saying that I am unhappy that lmer doesn't find the best answer - even though I suspect this situation i | Completely different results from lme() and lmer()
I agree with @DimitrisRizopoulos's answer, and have a few more points to make.
I will start by saying that I am unhappy that lmer doesn't find the best answer - even though I suspect this situation is probably limited to small, unusual (see below) data sets. One of th... | Completely different results from lme() and lmer()
I agree with @DimitrisRizopoulos's answer, and have a few more points to make.
I will start by saying that I am unhappy that lmer doesn't find the best answer - even though I suspect this situation i |
38,236 | How can I estimate the highest posterior density interval from a set of x,y values describing the PDF? | Since you have some example points from the PDF rather than a closed-form representation, and you're looking for a highest-density interval rather than a highest-density set, it's easiest to do this by brute force. Consider all $a$ and $b$ among the $x$-coordinates you have, check that $[a, b]$ has at least the desired... | How can I estimate the highest posterior density interval from a set of x,y values describing the PD | Since you have some example points from the PDF rather than a closed-form representation, and you're looking for a highest-density interval rather than a highest-density set, it's easiest to do this b | How can I estimate the highest posterior density interval from a set of x,y values describing the PDF?
Since you have some example points from the PDF rather than a closed-form representation, and you're looking for a highest-density interval rather than a highest-density set, it's easiest to do this by brute force. Co... | How can I estimate the highest posterior density interval from a set of x,y values describing the PD
Since you have some example points from the PDF rather than a closed-form representation, and you're looking for a highest-density interval rather than a highest-density set, it's easiest to do this b |
38,237 | How can I estimate the highest posterior density interval from a set of x,y values describing the PDF? | Depending on your needs, there are efficient solutions available. The purpose of this answer is to show how you can formulate the problem, encapsulate the key ideas, and implement them using capabilities offered by your programming environment.
Analysis
Given any density function $f$, all highest posterior density set... | How can I estimate the highest posterior density interval from a set of x,y values describing the PD | Depending on your needs, there are efficient solutions available. The purpose of this answer is to show how you can formulate the problem, encapsulate the key ideas, and implement them using capabili | How can I estimate the highest posterior density interval from a set of x,y values describing the PDF?
Depending on your needs, there are efficient solutions available. The purpose of this answer is to show how you can formulate the problem, encapsulate the key ideas, and implement them using capabilities offered by y... | How can I estimate the highest posterior density interval from a set of x,y values describing the PD
Depending on your needs, there are efficient solutions available. The purpose of this answer is to show how you can formulate the problem, encapsulate the key ideas, and implement them using capabili |
38,238 | How to simulate Likert-scale data in R? [closed] | To perform the simulation, here is a one line solution using the sample function:
sample(0:4, N, replace = TRUE, prob = c(0.1, 0.2, 0.4, 0.2, 0.1))
#where:
# 0:4 is the sequence of values (0 to 4 in this case)
# N is the number of samples (participants)
# replace = TRUE for sampling with replacement
# prob = c(0.1,... | How to simulate Likert-scale data in R? [closed] | To perform the simulation, here is a one line solution using the sample function:
sample(0:4, N, replace = TRUE, prob = c(0.1, 0.2, 0.4, 0.2, 0.1))
#where:
# 0:4 is the sequence of values (0 to 4 i | How to simulate Likert-scale data in R? [closed]
To perform the simulation, here is a one line solution using the sample function:
sample(0:4, N, replace = TRUE, prob = c(0.1, 0.2, 0.4, 0.2, 0.1))
#where:
# 0:4 is the sequence of values (0 to 4 in this case)
# N is the number of samples (participants)
# replace = TR... | How to simulate Likert-scale data in R? [closed]
To perform the simulation, here is a one line solution using the sample function:
sample(0:4, N, replace = TRUE, prob = c(0.1, 0.2, 0.4, 0.2, 0.1))
#where:
# 0:4 is the sequence of values (0 to 4 i |
38,239 | How to simulate Likert-scale data in R? [closed] | A likert scale, as the term is typically used, is just an ordinal rating scale. The phrase is often used for a single rating, which might have been called a likert item. Traditionally, the idea was that you would have a set of likert items that all measure the same thing and have the same measurement properties. The... | How to simulate Likert-scale data in R? [closed] | A likert scale, as the term is typically used, is just an ordinal rating scale. The phrase is often used for a single rating, which might have been called a likert item. Traditionally, the idea was | How to simulate Likert-scale data in R? [closed]
A likert scale, as the term is typically used, is just an ordinal rating scale. The phrase is often used for a single rating, which might have been called a likert item. Traditionally, the idea was that you would have a set of likert items that all measure the same thi... | How to simulate Likert-scale data in R? [closed]
A likert scale, as the term is typically used, is just an ordinal rating scale. The phrase is often used for a single rating, which might have been called a likert item. Traditionally, the idea was |
38,240 | How to simulate Likert-scale data in R? [closed] | One way to generate Likert data is according to a proportional odds model. Here, the underlying distribution of the (latent) response is a logistic random variable with a center $\mu$ that can vary as a function of one or more predictors. The latent variable is then thresholded into as many categories with an arbitrary... | How to simulate Likert-scale data in R? [closed] | One way to generate Likert data is according to a proportional odds model. Here, the underlying distribution of the (latent) response is a logistic random variable with a center $\mu$ that can vary as | How to simulate Likert-scale data in R? [closed]
One way to generate Likert data is according to a proportional odds model. Here, the underlying distribution of the (latent) response is a logistic random variable with a center $\mu$ that can vary as a function of one or more predictors. The latent variable is then thre... | How to simulate Likert-scale data in R? [closed]
One way to generate Likert data is according to a proportional odds model. Here, the underlying distribution of the (latent) response is a logistic random variable with a center $\mu$ that can vary as |
38,241 | How to simulate Likert-scale data in R? [closed] | Original source is here: http://ravshansk.com/articles/likert.html
The following formula works not only for Likert-scale models, but for any categorically distributed variables.
Suppose, you want to generate a 5-category data (x1, x2, x3, x4, x5) for N participants with probabilities (1/10, 2/10, 4/10, 2/10, 1/10). The... | How to simulate Likert-scale data in R? [closed] | Original source is here: http://ravshansk.com/articles/likert.html
The following formula works not only for Likert-scale models, but for any categorically distributed variables.
Suppose, you want to g | How to simulate Likert-scale data in R? [closed]
Original source is here: http://ravshansk.com/articles/likert.html
The following formula works not only for Likert-scale models, but for any categorically distributed variables.
Suppose, you want to generate a 5-category data (x1, x2, x3, x4, x5) for N participants with ... | How to simulate Likert-scale data in R? [closed]
Original source is here: http://ravshansk.com/articles/likert.html
The following formula works not only for Likert-scale models, but for any categorically distributed variables.
Suppose, you want to g |
38,242 | Etymology of "Adam" algorithm for gradient descent | On p.1 of the document you cite: "the name Adam is derived from adaptive moment estimation". | Etymology of "Adam" algorithm for gradient descent | On p.1 of the document you cite: "the name Adam is derived from adaptive moment estimation". | Etymology of "Adam" algorithm for gradient descent
On p.1 of the document you cite: "the name Adam is derived from adaptive moment estimation". | Etymology of "Adam" algorithm for gradient descent
On p.1 of the document you cite: "the name Adam is derived from adaptive moment estimation". |
38,243 | Etymology of "Adam" algorithm for gradient descent | Although Nick already answered the question, I would like to elaborate a bit.
From the introduction of Adam: A Method for Stochastic Optimization (the original Adam paper):
The method computes individual adaptive learning rates for
different parameters from estimates of first and second moments of the gradients; the... | Etymology of "Adam" algorithm for gradient descent | Although Nick already answered the question, I would like to elaborate a bit.
From the introduction of Adam: A Method for Stochastic Optimization (the original Adam paper):
The method computes indivi | Etymology of "Adam" algorithm for gradient descent
Although Nick already answered the question, I would like to elaborate a bit.
From the introduction of Adam: A Method for Stochastic Optimization (the original Adam paper):
The method computes individual adaptive learning rates for
different parameters from estimate... | Etymology of "Adam" algorithm for gradient descent
Although Nick already answered the question, I would like to elaborate a bit.
From the introduction of Adam: A Method for Stochastic Optimization (the original Adam paper):
The method computes indivi |
38,244 | Expected value of $1/x$ when $x$ follows a Beta distribution | First note that the pdf of a Beta$(\alpha, \beta)$ distribution is only defined for $\alpha, \beta > 0$. Which means that for when $\alpha \leq 0$ or $\beta \leq 0$
$$\int_0^1 \dfrac{x^{\alpha - 1} (1 - x)^{1 - \beta}}{B(\alpha, \beta)} $$
is not finite. Now,
\begin{align*}
E\left(\dfrac{1}{X} \right) & = \int_0^1 \df... | Expected value of $1/x$ when $x$ follows a Beta distribution | First note that the pdf of a Beta$(\alpha, \beta)$ distribution is only defined for $\alpha, \beta > 0$. Which means that for when $\alpha \leq 0$ or $\beta \leq 0$
$$\int_0^1 \dfrac{x^{\alpha - 1} ( | Expected value of $1/x$ when $x$ follows a Beta distribution
First note that the pdf of a Beta$(\alpha, \beta)$ distribution is only defined for $\alpha, \beta > 0$. Which means that for when $\alpha \leq 0$ or $\beta \leq 0$
$$\int_0^1 \dfrac{x^{\alpha - 1} (1 - x)^{1 - \beta}}{B(\alpha, \beta)} $$
is not finite. Now... | Expected value of $1/x$ when $x$ follows a Beta distribution
First note that the pdf of a Beta$(\alpha, \beta)$ distribution is only defined for $\alpha, \beta > 0$. Which means that for when $\alpha \leq 0$ or $\beta \leq 0$
$$\int_0^1 \dfrac{x^{\alpha - 1} ( |
38,245 | Expected value of $1/x$ when $x$ follows a Beta distribution | I want to point out another interesting solution method, which also generalizes the result to the expectation of $X^{-m}$ for integer $m=1,2,3,\dots$. I will use moment generating functions (mgf) and the results from the paper by N Cressie et.al http://amstat.tandfonline.com/doi/abs/10.1080/00031305.1981.10479334?journ... | Expected value of $1/x$ when $x$ follows a Beta distribution | I want to point out another interesting solution method, which also generalizes the result to the expectation of $X^{-m}$ for integer $m=1,2,3,\dots$. I will use moment generating functions (mgf) and | Expected value of $1/x$ when $x$ follows a Beta distribution
I want to point out another interesting solution method, which also generalizes the result to the expectation of $X^{-m}$ for integer $m=1,2,3,\dots$. I will use moment generating functions (mgf) and the results from the paper by N Cressie et.al http://amstat... | Expected value of $1/x$ when $x$ follows a Beta distribution
I want to point out another interesting solution method, which also generalizes the result to the expectation of $X^{-m}$ for integer $m=1,2,3,\dots$. I will use moment generating functions (mgf) and |
38,246 | Trying to understand the fitted vs residual plot? [duplicate] | According to the discussion in Draper and Smith's Applied Regression Analysis (3rd edition, roughly page 59), this residual plot may be used to check for violations in model assumptions particularly related to incorrect specification or presence of heteroscedasticity.
In the case that no violations are detects, the fig... | Trying to understand the fitted vs residual plot? [duplicate] | According to the discussion in Draper and Smith's Applied Regression Analysis (3rd edition, roughly page 59), this residual plot may be used to check for violations in model assumptions particularly r | Trying to understand the fitted vs residual plot? [duplicate]
According to the discussion in Draper and Smith's Applied Regression Analysis (3rd edition, roughly page 59), this residual plot may be used to check for violations in model assumptions particularly related to incorrect specification or presence of heterosce... | Trying to understand the fitted vs residual plot? [duplicate]
According to the discussion in Draper and Smith's Applied Regression Analysis (3rd edition, roughly page 59), this residual plot may be used to check for violations in model assumptions particularly r |
38,247 | Trying to understand the fitted vs residual plot? [duplicate] | To follow up on @mdewey's answer and disagree mildly with @jjet's: the scale-location plot in the lower left is best for evaluating homo/heteroscedasticity. Two reasons:
as raised by @mdewey: it's easier to judge whether the slope of a line than the amount of spread of a point cloud, and easier to fit a nonparametric ... | Trying to understand the fitted vs residual plot? [duplicate] | To follow up on @mdewey's answer and disagree mildly with @jjet's: the scale-location plot in the lower left is best for evaluating homo/heteroscedasticity. Two reasons:
as raised by @mdewey: it's ea | Trying to understand the fitted vs residual plot? [duplicate]
To follow up on @mdewey's answer and disagree mildly with @jjet's: the scale-location plot in the lower left is best for evaluating homo/heteroscedasticity. Two reasons:
as raised by @mdewey: it's easier to judge whether the slope of a line than the amount ... | Trying to understand the fitted vs residual plot? [duplicate]
To follow up on @mdewey's answer and disagree mildly with @jjet's: the scale-location plot in the lower left is best for evaluating homo/heteroscedasticity. Two reasons:
as raised by @mdewey: it's ea |
38,248 | Trying to understand the fitted vs residual plot? [duplicate] | If you are looking at the top left plot then yes. However the best plot for what you intend is the bottom left one which folds the residuals about the horizontal axis in the first one so that the smoothed line drawn on that plot should be horizontal if there is no relation between scale and location. In your case it lo... | Trying to understand the fitted vs residual plot? [duplicate] | If you are looking at the top left plot then yes. However the best plot for what you intend is the bottom left one which folds the residuals about the horizontal axis in the first one so that the smoo | Trying to understand the fitted vs residual plot? [duplicate]
If you are looking at the top left plot then yes. However the best plot for what you intend is the bottom left one which folds the residuals about the horizontal axis in the first one so that the smoothed line drawn on that plot should be horizontal if there... | Trying to understand the fitted vs residual plot? [duplicate]
If you are looking at the top left plot then yes. However the best plot for what you intend is the bottom left one which folds the residuals about the horizontal axis in the first one so that the smoo |
38,249 | Trying to understand the fitted vs residual plot? [duplicate] | The second point is best evaluated using the top-left plot. Basically, you want to check to see whether the spread of the residuals is the same at all points along the x-axis. If it is, then you'll see a band of points that move horizontally along the x-axis. This would then suggest little evidence of heteroscedasticit... | Trying to understand the fitted vs residual plot? [duplicate] | The second point is best evaluated using the top-left plot. Basically, you want to check to see whether the spread of the residuals is the same at all points along the x-axis. If it is, then you'll se | Trying to understand the fitted vs residual plot? [duplicate]
The second point is best evaluated using the top-left plot. Basically, you want to check to see whether the spread of the residuals is the same at all points along the x-axis. If it is, then you'll see a band of points that move horizontally along the x-axis... | Trying to understand the fitted vs residual plot? [duplicate]
The second point is best evaluated using the top-left plot. Basically, you want to check to see whether the spread of the residuals is the same at all points along the x-axis. If it is, then you'll se |
38,250 | Using lasso for feature selection, followed by a non-regularized regression | Note that there exist multiple iterative LASSO procedures, so in general, it is not necessarily true that you should stick with the first LASSO estimates.
For example:
Post-LASSO-OLS: see Belloni, Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models, Bernoulli 19(2), 2013, 521–547... | Using lasso for feature selection, followed by a non-regularized regression | Note that there exist multiple iterative LASSO procedures, so in general, it is not necessarily true that you should stick with the first LASSO estimates.
For example:
Post-LASSO-OLS: see Belloni, C | Using lasso for feature selection, followed by a non-regularized regression
Note that there exist multiple iterative LASSO procedures, so in general, it is not necessarily true that you should stick with the first LASSO estimates.
For example:
Post-LASSO-OLS: see Belloni, Chernozhukov (2013) Least squares after model... | Using lasso for feature selection, followed by a non-regularized regression
Note that there exist multiple iterative LASSO procedures, so in general, it is not necessarily true that you should stick with the first LASSO estimates.
For example:
Post-LASSO-OLS: see Belloni, C |
38,251 | Using lasso for feature selection, followed by a non-regularized regression | Performing some variable selection (e.g. with LASSO with the smoothing parameter chosen by cross-validation or some of the other alternatives like the elastic net etc.) and then fitting a model on the same data as if no variable selection had happened is always inappropriate. Why not look at the results from LASSO? As ... | Using lasso for feature selection, followed by a non-regularized regression | Performing some variable selection (e.g. with LASSO with the smoothing parameter chosen by cross-validation or some of the other alternatives like the elastic net etc.) and then fitting a model on the | Using lasso for feature selection, followed by a non-regularized regression
Performing some variable selection (e.g. with LASSO with the smoothing parameter chosen by cross-validation or some of the other alternatives like the elastic net etc.) and then fitting a model on the same data as if no variable selection had h... | Using lasso for feature selection, followed by a non-regularized regression
Performing some variable selection (e.g. with LASSO with the smoothing parameter chosen by cross-validation or some of the other alternatives like the elastic net etc.) and then fitting a model on the |
38,252 | Looking for function to fit sigmoid-like curve | I think smoothing splines with small degrees of freedom would do the trick. Here's an example in R:
The R code:
txt <- "| 0 | 0 |
| 1.6366666667 | -12.2012787905 |
| 3.2733333333 | -13.7833876716 |
| 4.91 | -10.5943208589 |
| 6.5466666667 | -1.3584575518 |
| 8.1833333333 | 8.1590... | Looking for function to fit sigmoid-like curve | I think smoothing splines with small degrees of freedom would do the trick. Here's an example in R:
The R code:
txt <- "| 0 | 0 |
| 1.6366666667 | -12.2012787905 |
| 3.27333 | Looking for function to fit sigmoid-like curve
I think smoothing splines with small degrees of freedom would do the trick. Here's an example in R:
The R code:
txt <- "| 0 | 0 |
| 1.6366666667 | -12.2012787905 |
| 3.2733333333 | -13.7833876716 |
| 4.91 | -10.5943208589 |
| 6.54666666... | Looking for function to fit sigmoid-like curve
I think smoothing splines with small degrees of freedom would do the trick. Here's an example in R:
The R code:
txt <- "| 0 | 0 |
| 1.6366666667 | -12.2012787905 |
| 3.27333 |
38,253 | Looking for function to fit sigmoid-like curve | To fit a sigmoid-like function in a nonparametric way, we could use a monotone spline. This is implemented in the R package (all R packages here referenced are on CRAN) splines2. I will borrow some R code from the answer by @Chaconne, and modify it for my needs.
splines2 offers the functions mSpline, implementing M-... | Looking for function to fit sigmoid-like curve | To fit a sigmoid-like function in a nonparametric way, we could use a monotone spline. This is implemented in the R package (all R packages here referenced are on CRAN) splines2. I will borrow some | Looking for function to fit sigmoid-like curve
To fit a sigmoid-like function in a nonparametric way, we could use a monotone spline. This is implemented in the R package (all R packages here referenced are on CRAN) splines2. I will borrow some R code from the answer by @Chaconne, and modify it for my needs.
splines... | Looking for function to fit sigmoid-like curve
To fit a sigmoid-like function in a nonparametric way, we could use a monotone spline. This is implemented in the R package (all R packages here referenced are on CRAN) splines2. I will borrow some |
38,254 | Looking for function to fit sigmoid-like curve | The curve you show looks more like a cubic function, $ax^3+bx^2+cx+d$, as the ends turn up and down, rather than extend flat/horizontal. Or something like this, made with a polynomial trend line in Excel:
But otherwise, if you want the ends to extend horizontal, there are many sigmoidal CDF probability distributions ... | Looking for function to fit sigmoid-like curve | The curve you show looks more like a cubic function, $ax^3+bx^2+cx+d$, as the ends turn up and down, rather than extend flat/horizontal. Or something like this, made with a polynomial trend line in E | Looking for function to fit sigmoid-like curve
The curve you show looks more like a cubic function, $ax^3+bx^2+cx+d$, as the ends turn up and down, rather than extend flat/horizontal. Or something like this, made with a polynomial trend line in Excel:
But otherwise, if you want the ends to extend horizontal, there ar... | Looking for function to fit sigmoid-like curve
The curve you show looks more like a cubic function, $ax^3+bx^2+cx+d$, as the ends turn up and down, rather than extend flat/horizontal. Or something like this, made with a polynomial trend line in E |
38,255 | Looking for function to fit sigmoid-like curve | You can use the sigmoid() function from the {pracma} package in R. The function will fit a sigmoidal curve to a numeric vector. If you don't care what function fits the data, I would recommend the gam() function from the {mgcv} package in R. It fits a smoothing function to the data using spline regression (the default ... | Looking for function to fit sigmoid-like curve | You can use the sigmoid() function from the {pracma} package in R. The function will fit a sigmoidal curve to a numeric vector. If you don't care what function fits the data, I would recommend the gam | Looking for function to fit sigmoid-like curve
You can use the sigmoid() function from the {pracma} package in R. The function will fit a sigmoidal curve to a numeric vector. If you don't care what function fits the data, I would recommend the gam() function from the {mgcv} package in R. It fits a smoothing function to... | Looking for function to fit sigmoid-like curve
You can use the sigmoid() function from the {pracma} package in R. The function will fit a sigmoidal curve to a numeric vector. If you don't care what function fits the data, I would recommend the gam |
38,256 | Looking for function to fit sigmoid-like curve | Along with the other suggestions, a Gompertz growth curve would also fit this data. Here's what Wikipedia has to say about it:
A Gompertz curve or Gompertz function, named after Benjamin Gompertz,
is a sigmoid function. It is a type of mathematical model for a time
series, where growth is slowest at the start and ... | Looking for function to fit sigmoid-like curve | Along with the other suggestions, a Gompertz growth curve would also fit this data. Here's what Wikipedia has to say about it:
A Gompertz curve or Gompertz function, named after Benjamin Gompertz,
| Looking for function to fit sigmoid-like curve
Along with the other suggestions, a Gompertz growth curve would also fit this data. Here's what Wikipedia has to say about it:
A Gompertz curve or Gompertz function, named after Benjamin Gompertz,
is a sigmoid function. It is a type of mathematical model for a time
se... | Looking for function to fit sigmoid-like curve
Along with the other suggestions, a Gompertz growth curve would also fit this data. Here's what Wikipedia has to say about it:
A Gompertz curve or Gompertz function, named after Benjamin Gompertz,
|
38,257 | Looking for function to fit sigmoid-like curve | But if you don't want to extrapolate (I think interpolate would be a better word) between the points, then no parametric function will give you better than what you have got, that is a straight line between your points. If you have a parametric model with as many parameters as you have observations, it will simply repl... | Looking for function to fit sigmoid-like curve | But if you don't want to extrapolate (I think interpolate would be a better word) between the points, then no parametric function will give you better than what you have got, that is a straight line b | Looking for function to fit sigmoid-like curve
But if you don't want to extrapolate (I think interpolate would be a better word) between the points, then no parametric function will give you better than what you have got, that is a straight line between your points. If you have a parametric model with as many parameter... | Looking for function to fit sigmoid-like curve
But if you don't want to extrapolate (I think interpolate would be a better word) between the points, then no parametric function will give you better than what you have got, that is a straight line b |
38,258 | Looking for function to fit sigmoid-like curve | If you're trying to obtain a CDF-like function (non-zero), you could use weighted Weibull curve of the form
$y=A(1-e^{-(x/\alpha)^\beta})$
When I do this, I obtain roughly $A = 100$, $\alpha=12.3$, and $\beta=9.0$
(the resultant $\beta$ is much higher than I've typically run across for lifetime distributions) | Looking for function to fit sigmoid-like curve | If you're trying to obtain a CDF-like function (non-zero), you could use weighted Weibull curve of the form
$y=A(1-e^{-(x/\alpha)^\beta})$
When I do this, I obtain roughly $A = 100$, $\alpha=12.3$, an | Looking for function to fit sigmoid-like curve
If you're trying to obtain a CDF-like function (non-zero), you could use weighted Weibull curve of the form
$y=A(1-e^{-(x/\alpha)^\beta})$
When I do this, I obtain roughly $A = 100$, $\alpha=12.3$, and $\beta=9.0$
(the resultant $\beta$ is much higher than I've typically r... | Looking for function to fit sigmoid-like curve
If you're trying to obtain a CDF-like function (non-zero), you could use weighted Weibull curve of the form
$y=A(1-e^{-(x/\alpha)^\beta})$
When I do this, I obtain roughly $A = 100$, $\alpha=12.3$, an |
38,259 | Sample size calculation for correlation study | I would be wary of using the published value of r, 0.47, as the basis for your sample size calculations.
What if the true correlation is say 0.25? If that were the true population correlation, would you want your study to find a "significant" result? If so, compute the sample size for r = 0.25 (or even smaller). More ... | Sample size calculation for correlation study | I would be wary of using the published value of r, 0.47, as the basis for your sample size calculations.
What if the true correlation is say 0.25? If that were the true population correlation, would | Sample size calculation for correlation study
I would be wary of using the published value of r, 0.47, as the basis for your sample size calculations.
What if the true correlation is say 0.25? If that were the true population correlation, would you want your study to find a "significant" result? If so, compute the sam... | Sample size calculation for correlation study
I would be wary of using the published value of r, 0.47, as the basis for your sample size calculations.
What if the true correlation is say 0.25? If that were the true population correlation, would |
38,260 | Sample size calculation for correlation study | To run the power analysis, you need to know three of the four to calculate the last one:
Number of observations
Effect size (correlation coefficient)
Significance level
Power
You have stated the effect size (correlation coefficient) in your question to be 0.47. Next, let's decide to use the conventional significance ... | Sample size calculation for correlation study | To run the power analysis, you need to know three of the four to calculate the last one:
Number of observations
Effect size (correlation coefficient)
Significance level
Power
You have stated the eff | Sample size calculation for correlation study
To run the power analysis, you need to know three of the four to calculate the last one:
Number of observations
Effect size (correlation coefficient)
Significance level
Power
You have stated the effect size (correlation coefficient) in your question to be 0.47. Next, let'... | Sample size calculation for correlation study
To run the power analysis, you need to know three of the four to calculate the last one:
Number of observations
Effect size (correlation coefficient)
Significance level
Power
You have stated the eff |
38,261 | Sample size calculation for correlation study | Here is another example using GPower expressed in graph, with sample size versus power:
A sample of 45 seems to be reasonable, with power > 0.9. | Sample size calculation for correlation study | Here is another example using GPower expressed in graph, with sample size versus power:
A sample of 45 seems to be reasonable, with power > 0.9. | Sample size calculation for correlation study
Here is another example using GPower expressed in graph, with sample size versus power:
A sample of 45 seems to be reasonable, with power > 0.9. | Sample size calculation for correlation study
Here is another example using GPower expressed in graph, with sample size versus power:
A sample of 45 seems to be reasonable, with power > 0.9. |
38,262 | Should I use t-test on highly skewed and discrete data? | Highly discrete and skew variables can exhibit some particular issues in their t-statistics:
For example, consider something like this:
(it has a bit more of a tail out to the right, that's been cut off, going out to 90-something)
The distribution of two-sample t-statistics for samples of size 50 look something like t... | Should I use t-test on highly skewed and discrete data? | Highly discrete and skew variables can exhibit some particular issues in their t-statistics:
For example, consider something like this:
(it has a bit more of a tail out to the right, that's been cut | Should I use t-test on highly skewed and discrete data?
Highly discrete and skew variables can exhibit some particular issues in their t-statistics:
For example, consider something like this:
(it has a bit more of a tail out to the right, that's been cut off, going out to 90-something)
The distribution of two-sample t... | Should I use t-test on highly skewed and discrete data?
Highly discrete and skew variables can exhibit some particular issues in their t-statistics:
For example, consider something like this:
(it has a bit more of a tail out to the right, that's been cut |
38,263 | Should I use t-test on highly skewed and discrete data? | You should not use the t-test or even Welch's modified t-test on very skewed data, because these tests tend to be conservative (e.g., alpha and power of these tests can be reduced; Zimmerman and Zumbo, 1993).
Then which test should you use? Your response variable is discrete count data with many 0's, and you want to co... | Should I use t-test on highly skewed and discrete data? | You should not use the t-test or even Welch's modified t-test on very skewed data, because these tests tend to be conservative (e.g., alpha and power of these tests can be reduced; Zimmerman and Zumbo | Should I use t-test on highly skewed and discrete data?
You should not use the t-test or even Welch's modified t-test on very skewed data, because these tests tend to be conservative (e.g., alpha and power of these tests can be reduced; Zimmerman and Zumbo, 1993).
Then which test should you use? Your response variable ... | Should I use t-test on highly skewed and discrete data?
You should not use the t-test or even Welch's modified t-test on very skewed data, because these tests tend to be conservative (e.g., alpha and power of these tests can be reduced; Zimmerman and Zumbo |
38,264 | Should I use t-test on highly skewed and discrete data? | To $T$ or not to $T$ -- is that the question?
I would suggest backing off for a moment and asking yourself, "What IS the question?" Is the question, "Are the means of populations 1 and 2 the same?", or is the question, "Is the usage distribution the same in populations 1 and 2?", or is the question, "Are the medians o... | Should I use t-test on highly skewed and discrete data? | To $T$ or not to $T$ -- is that the question?
I would suggest backing off for a moment and asking yourself, "What IS the question?" Is the question, "Are the means of populations 1 and 2 the same?", | Should I use t-test on highly skewed and discrete data?
To $T$ or not to $T$ -- is that the question?
I would suggest backing off for a moment and asking yourself, "What IS the question?" Is the question, "Are the means of populations 1 and 2 the same?", or is the question, "Is the usage distribution the same in popul... | Should I use t-test on highly skewed and discrete data?
To $T$ or not to $T$ -- is that the question?
I would suggest backing off for a moment and asking yourself, "What IS the question?" Is the question, "Are the means of populations 1 and 2 the same?", |
38,265 | Should I use t-test on highly skewed and discrete data? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
While it will come with its own set of limitations, pr... | Should I use t-test on highly skewed and discrete data? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Should I use t-test on highly skewed and discrete data?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | Should I use t-test on highly skewed and discrete data?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
38,266 | Interpreting one- and two-tailed tests | You don't choose a one-tailed test based on near-significance in a two-tailed test.
You don't choose the direction of a one-tailed test based on directional information from the data.
Or at the least, if you do those things, you must also double the resulting p-value.
A one tailed test - if you do one at all - must be ... | Interpreting one- and two-tailed tests | You don't choose a one-tailed test based on near-significance in a two-tailed test.
You don't choose the direction of a one-tailed test based on directional information from the data.
Or at the least, | Interpreting one- and two-tailed tests
You don't choose a one-tailed test based on near-significance in a two-tailed test.
You don't choose the direction of a one-tailed test based on directional information from the data.
Or at the least, if you do those things, you must also double the resulting p-value.
A one tailed... | Interpreting one- and two-tailed tests
You don't choose a one-tailed test based on near-significance in a two-tailed test.
You don't choose the direction of a one-tailed test based on directional information from the data.
Or at the least, |
38,267 | Interpreting one- and two-tailed tests | Report the results that correspond to your hypothesis, which should be one- or two-tailed, not both. You should be able to decide which is appropriate on a theoretical basis before performing the test. Once you've decided, report the p value as you calculated it. If it's very small, consider the advice in responses to ... | Interpreting one- and two-tailed tests | Report the results that correspond to your hypothesis, which should be one- or two-tailed, not both. You should be able to decide which is appropriate on a theoretical basis before performing the test | Interpreting one- and two-tailed tests
Report the results that correspond to your hypothesis, which should be one- or two-tailed, not both. You should be able to decide which is appropriate on a theoretical basis before performing the test. Once you've decided, report the p value as you calculated it. If it's very smal... | Interpreting one- and two-tailed tests
Report the results that correspond to your hypothesis, which should be one- or two-tailed, not both. You should be able to decide which is appropriate on a theoretical basis before performing the test |
38,268 | How to deal with a skewed class in binary classification having many features? | First 18 isn't a lot of features at all and you should see if you can get more data. Google uses a ridiculous number of features in their ad targeting and takes a different online/game theoretical approach to choosing what ad to show to the audience
Second, skewed class labels like this are a common problem. Search ter... | How to deal with a skewed class in binary classification having many features? | First 18 isn't a lot of features at all and you should see if you can get more data. Google uses a ridiculous number of features in their ad targeting and takes a different online/game theoretical app | How to deal with a skewed class in binary classification having many features?
First 18 isn't a lot of features at all and you should see if you can get more data. Google uses a ridiculous number of features in their ad targeting and takes a different online/game theoretical approach to choosing what ad to show to the ... | How to deal with a skewed class in binary classification having many features?
First 18 isn't a lot of features at all and you should see if you can get more data. Google uses a ridiculous number of features in their ad targeting and takes a different online/game theoretical app |
38,269 | How to deal with a skewed class in binary classification having many features? | I'll try to add a little intuition as to why you get such results. Considering all such classification tasks, the best result would be to predict 100% of the results correctly, right? In your case, with 0.4% vs 99.6% class balance, if you predict 0 for every row, then you automatically get to be 99.6% right. That's is ... | How to deal with a skewed class in binary classification having many features? | I'll try to add a little intuition as to why you get such results. Considering all such classification tasks, the best result would be to predict 100% of the results correctly, right? In your case, wi | How to deal with a skewed class in binary classification having many features?
I'll try to add a little intuition as to why you get such results. Considering all such classification tasks, the best result would be to predict 100% of the results correctly, right? In your case, with 0.4% vs 99.6% class balance, if you pr... | How to deal with a skewed class in binary classification having many features?
I'll try to add a little intuition as to why you get such results. Considering all such classification tasks, the best result would be to predict 100% of the results correctly, right? In your case, wi |
38,270 | How to deal with a skewed class in binary classification having many features? | The problem is the skew of the class balance. The simplest thing you could try would be to reduce the size of the majority class of your training set. Just randomly sample (without replacement) N instances form the majority class, where N is the number of instances in the minority class. This is called 'undersampling.' | How to deal with a skewed class in binary classification having many features? | The problem is the skew of the class balance. The simplest thing you could try would be to reduce the size of the majority class of your training set. Just randomly sample (without replacement) N inst | How to deal with a skewed class in binary classification having many features?
The problem is the skew of the class balance. The simplest thing you could try would be to reduce the size of the majority class of your training set. Just randomly sample (without replacement) N instances form the majority class, where N is... | How to deal with a skewed class in binary classification having many features?
The problem is the skew of the class balance. The simplest thing you could try would be to reduce the size of the majority class of your training set. Just randomly sample (without replacement) N inst |
38,271 | Graphical Models and Explaining Away? | Yes, this is because the $\alpha, \beta$ node $d$-separates $m_2$ and $y_3$. See Probabilistic Reasoning in Intelligent Systems for an explanation of $d$-separation. | Graphical Models and Explaining Away? | Yes, this is because the $\alpha, \beta$ node $d$-separates $m_2$ and $y_3$. See Probabilistic Reasoning in Intelligent Systems for an explanation of $d$-separation. | Graphical Models and Explaining Away?
Yes, this is because the $\alpha, \beta$ node $d$-separates $m_2$ and $y_3$. See Probabilistic Reasoning in Intelligent Systems for an explanation of $d$-separation. | Graphical Models and Explaining Away?
Yes, this is because the $\alpha, \beta$ node $d$-separates $m_2$ and $y_3$. See Probabilistic Reasoning in Intelligent Systems for an explanation of $d$-separation. |
38,272 | Graphical Models and Explaining Away? | Another intuitive example of why two sibling nodes are independent given their parent:
Imagine $A$ and $B$ are two guys living in the same city.
Whether A gets wet or not depends on whether it rains or not. Same for B.
Now, if don't know whether it rained or not but we observed that $A$ is wet, we are going to thin... | Graphical Models and Explaining Away? | Another intuitive example of why two sibling nodes are independent given their parent:
Imagine $A$ and $B$ are two guys living in the same city.
Whether A gets wet or not depends on whether it rain | Graphical Models and Explaining Away?
Another intuitive example of why two sibling nodes are independent given their parent:
Imagine $A$ and $B$ are two guys living in the same city.
Whether A gets wet or not depends on whether it rains or not. Same for B.
Now, if don't know whether it rained or not but we observed... | Graphical Models and Explaining Away?
Another intuitive example of why two sibling nodes are independent given their parent:
Imagine $A$ and $B$ are two guys living in the same city.
Whether A gets wet or not depends on whether it rain |
38,273 | Graphical Models and Explaining Away? | I like to think about it like this:
What additional information does knowing $y_3=X$ actually give you?
Now the additional is key here. Imagine you knew nothing but $y_3=X$. Then in order:
We can learn about $m_3$ by asking what it needs to make $y_3=X$ more likely
We can learn about $\alpha,\beta$ by asking ourselve... | Graphical Models and Explaining Away? | I like to think about it like this:
What additional information does knowing $y_3=X$ actually give you?
Now the additional is key here. Imagine you knew nothing but $y_3=X$. Then in order:
We can le | Graphical Models and Explaining Away?
I like to think about it like this:
What additional information does knowing $y_3=X$ actually give you?
Now the additional is key here. Imagine you knew nothing but $y_3=X$. Then in order:
We can learn about $m_3$ by asking what it needs to make $y_3=X$ more likely
We can learn a... | Graphical Models and Explaining Away?
I like to think about it like this:
What additional information does knowing $y_3=X$ actually give you?
Now the additional is key here. Imagine you knew nothing but $y_3=X$. Then in order:
We can le |
38,274 | Graphical Models and Explaining Away? | \begin{align}
p(m_2|\alpha,\beta,y_3)&=\frac{p(m_2,y_3|\alpha, \beta)}{p(y_3|\alpha,\beta)}\\
&= \frac{p(m_2|\alpha,\beta)p(y_3|\alpha, \beta)}{p(y_3|\alpha, \beta)}\\
&=p(m_2|\alpha,\beta)
\end{align}
$p(m_2,y_3|\alpha, \beta)=p(m_2|\alpha,\beta)p(y_3|\alpha, \beta)$ holds because $m_2$, $y_3$ and $\alpha, \beta$ for... | Graphical Models and Explaining Away? | \begin{align}
p(m_2|\alpha,\beta,y_3)&=\frac{p(m_2,y_3|\alpha, \beta)}{p(y_3|\alpha,\beta)}\\
&= \frac{p(m_2|\alpha,\beta)p(y_3|\alpha, \beta)}{p(y_3|\alpha, \beta)}\\
&=p(m_2|\alpha,\beta)
\end{align | Graphical Models and Explaining Away?
\begin{align}
p(m_2|\alpha,\beta,y_3)&=\frac{p(m_2,y_3|\alpha, \beta)}{p(y_3|\alpha,\beta)}\\
&= \frac{p(m_2|\alpha,\beta)p(y_3|\alpha, \beta)}{p(y_3|\alpha, \beta)}\\
&=p(m_2|\alpha,\beta)
\end{align}
$p(m_2,y_3|\alpha, \beta)=p(m_2|\alpha,\beta)p(y_3|\alpha, \beta)$ holds because... | Graphical Models and Explaining Away?
\begin{align}
p(m_2|\alpha,\beta,y_3)&=\frac{p(m_2,y_3|\alpha, \beta)}{p(y_3|\alpha,\beta)}\\
&= \frac{p(m_2|\alpha,\beta)p(y_3|\alpha, \beta)}{p(y_3|\alpha, \beta)}\\
&=p(m_2|\alpha,\beta)
\end{align |
38,275 | How to store the standard errors with the lm() function in R? [closed] | Check the object that summary(reg) returns. You find then that
> str(summary(reg)$coef)
...
> X <- summary(reg)$coef
> X[,2]
(Intercept) x
0.03325738 0.05558073
gives you what you want. Or, if you calculate them yourself (as @caracal showed in the comments) :
sqrt(diag(vcov(reg))) | How to store the standard errors with the lm() function in R? [closed] | Check the object that summary(reg) returns. You find then that
> str(summary(reg)$coef)
...
> X <- summary(reg)$coef
> X[,2]
(Intercept) x
0.03325738 0.05558073
gives you what you want. | How to store the standard errors with the lm() function in R? [closed]
Check the object that summary(reg) returns. You find then that
> str(summary(reg)$coef)
...
> X <- summary(reg)$coef
> X[,2]
(Intercept) x
0.03325738 0.05558073
gives you what you want. Or, if you calculate them yourself (as @caracal ... | How to store the standard errors with the lm() function in R? [closed]
Check the object that summary(reg) returns. You find then that
> str(summary(reg)$coef)
...
> X <- summary(reg)$coef
> X[,2]
(Intercept) x
0.03325738 0.05558073
gives you what you want. |
38,276 | How to store the standard errors with the lm() function in R? [closed] | Somewhere Doug Bates once mentioned that accessor functions are preferable, so I'd do
R> example(lm) ## to create lm.D9 object
[...]
R> coef(summary(lm.D9))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.032 0.220218 22.85012 9.54713e-15
groupTrt -0.371 0.311435 -1.19126 2.49023e-01
R... | How to store the standard errors with the lm() function in R? [closed] | Somewhere Doug Bates once mentioned that accessor functions are preferable, so I'd do
R> example(lm) ## to create lm.D9 object
[...]
R> coef(summary(lm.D9))
Estimate Std. Error t value | How to store the standard errors with the lm() function in R? [closed]
Somewhere Doug Bates once mentioned that accessor functions are preferable, so I'd do
R> example(lm) ## to create lm.D9 object
[...]
R> coef(summary(lm.D9))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.032 0.220218 22.8... | How to store the standard errors with the lm() function in R? [closed]
Somewhere Doug Bates once mentioned that accessor functions are preferable, so I'd do
R> example(lm) ## to create lm.D9 object
[...]
R> coef(summary(lm.D9))
Estimate Std. Error t value |
38,277 | How to store the standard errors with the lm() function in R? [closed] | Following on @Joris Meys answer for how to calculate std. error manually.
#Std. Error = residual variance / variable variance =
sqrt(diag(vcov(reg)))
#where vcov(reg) =
summary(reg)$cov.unscaled * summary(reg)$sigma^2
#where summary(reg)$cov.unscaled = 1/(variable variance) (diagonal of precision matrix) =
solve(t(x... | How to store the standard errors with the lm() function in R? [closed] | Following on @Joris Meys answer for how to calculate std. error manually.
#Std. Error = residual variance / variable variance =
sqrt(diag(vcov(reg)))
#where vcov(reg) =
summary(reg)$cov.unscaled * s | How to store the standard errors with the lm() function in R? [closed]
Following on @Joris Meys answer for how to calculate std. error manually.
#Std. Error = residual variance / variable variance =
sqrt(diag(vcov(reg)))
#where vcov(reg) =
summary(reg)$cov.unscaled * summary(reg)$sigma^2
#where summary(reg)$cov.unsc... | How to store the standard errors with the lm() function in R? [closed]
Following on @Joris Meys answer for how to calculate std. error manually.
#Std. Error = residual variance / variable variance =
sqrt(diag(vcov(reg)))
#where vcov(reg) =
summary(reg)$cov.unscaled * s |
38,278 | Why is a "Correction" Required in Multiple Hypothesis Testing? | This is a tricky topic: when exactly do you correct for multiple testing? The two extremes are both problematic:
never correcting for multiple testing will result in too many false positives,
always correcting for multiple testing seems impossible, e.g. if, over your carreer as a statistician you perform $1\,000$ (whi... | Why is a "Correction" Required in Multiple Hypothesis Testing? | This is a tricky topic: when exactly do you correct for multiple testing? The two extremes are both problematic:
never correcting for multiple testing will result in too many false positives,
always | Why is a "Correction" Required in Multiple Hypothesis Testing?
This is a tricky topic: when exactly do you correct for multiple testing? The two extremes are both problematic:
never correcting for multiple testing will result in too many false positives,
always correcting for multiple testing seems impossible, e.g. if... | Why is a "Correction" Required in Multiple Hypothesis Testing?
This is a tricky topic: when exactly do you correct for multiple testing? The two extremes are both problematic:
never correcting for multiple testing will result in too many false positives,
always |
38,279 | Why is a "Correction" Required in Multiple Hypothesis Testing? | There is a lot of arbitrariness in certain statistical practices. As other responses and previous discussions in the literature point out, it's very hard to justify the tradition that you correct for multiple comparisons that you do within one study/one paper. This is a convention that has developed, but honestly has a... | Why is a "Correction" Required in Multiple Hypothesis Testing? | There is a lot of arbitrariness in certain statistical practices. As other responses and previous discussions in the literature point out, it's very hard to justify the tradition that you correct for | Why is a "Correction" Required in Multiple Hypothesis Testing?
There is a lot of arbitrariness in certain statistical practices. As other responses and previous discussions in the literature point out, it's very hard to justify the tradition that you correct for multiple comparisons that you do within one study/one pap... | Why is a "Correction" Required in Multiple Hypothesis Testing?
There is a lot of arbitrariness in certain statistical practices. As other responses and previous discussions in the literature point out, it's very hard to justify the tradition that you correct for |
38,280 | Why is a "Correction" Required in Multiple Hypothesis Testing? | I'm frequently asked when multiple comparison adjustment should be used. Then I start talking about false discovery rate, type-I errors and so on, to conclude that I was not fully understood. So I came with a following example (maybe "show don't tell" rule applies to data analysis too ;) ):
Imagine, you compare three p... | Why is a "Correction" Required in Multiple Hypothesis Testing? | I'm frequently asked when multiple comparison adjustment should be used. Then I start talking about false discovery rate, type-I errors and so on, to conclude that I was not fully understood. So I cam | Why is a "Correction" Required in Multiple Hypothesis Testing?
I'm frequently asked when multiple comparison adjustment should be used. Then I start talking about false discovery rate, type-I errors and so on, to conclude that I was not fully understood. So I came with a following example (maybe "show don't tell" rule ... | Why is a "Correction" Required in Multiple Hypothesis Testing?
I'm frequently asked when multiple comparison adjustment should be used. Then I start talking about false discovery rate, type-I errors and so on, to conclude that I was not fully understood. So I cam |
38,281 | Understand the illustration of the curse of dimensionality? | Let's look at the first few dimensions.
For $d=1$, if examples are laid out on a regular grid, this just means that they are at equal distances on a straight line, e.g., at the integers. We can assume that our test example $x_t$ is at the origin, $x_t=0$. There are two nearest neighbors with equal distance $1$, namel... | Understand the illustration of the curse of dimensionality? | Let's look at the first few dimensions.
For $d=1$, if examples are laid out on a regular grid, this just means that they are at equal distances on a straight line, e.g., at the integers. We can assum | Understand the illustration of the curse of dimensionality?
Let's look at the first few dimensions.
For $d=1$, if examples are laid out on a regular grid, this just means that they are at equal distances on a straight line, e.g., at the integers. We can assume that our test example $x_t$ is at the origin, $x_t=0$. Th... | Understand the illustration of the curse of dimensionality?
Let's look at the first few dimensions.
For $d=1$, if examples are laid out on a regular grid, this just means that they are at equal distances on a straight line, e.g., at the integers. We can assum |
38,282 | Understand the illustration of the curse of dimensionality? | Especially, why If the grid is d-dimensional, $x_t$’s 2d nearest examples are all at the same distance from it? Could anyone please visualize the illustration(if possible)?
The example speaks about a regular grid.
In a regular grid you have for each grid line (on each dimension) two neighbouring nodes one at +1 and on... | Understand the illustration of the curse of dimensionality? | Especially, why If the grid is d-dimensional, $x_t$’s 2d nearest examples are all at the same distance from it? Could anyone please visualize the illustration(if possible)?
The example speaks about a | Understand the illustration of the curse of dimensionality?
Especially, why If the grid is d-dimensional, $x_t$’s 2d nearest examples are all at the same distance from it? Could anyone please visualize the illustration(if possible)?
The example speaks about a regular grid.
In a regular grid you have for each grid line... | Understand the illustration of the curse of dimensionality?
Especially, why If the grid is d-dimensional, $x_t$’s 2d nearest examples are all at the same distance from it? Could anyone please visualize the illustration(if possible)?
The example speaks about a |
38,283 | Understand the illustration of the curse of dimensionality? | I think @Stephan Kolassa explains well what the authors meant in that paragraph. The theoretical basis for "in high dimensions all samples look alike" is layed out in section 3.4 Instability Result of this paper.
Skipping the proof on page 6, essentially
... all points converge to the same distance from the query poin... | Understand the illustration of the curse of dimensionality? | I think @Stephan Kolassa explains well what the authors meant in that paragraph. The theoretical basis for "in high dimensions all samples look alike" is layed out in section 3.4 Instability Result of | Understand the illustration of the curse of dimensionality?
I think @Stephan Kolassa explains well what the authors meant in that paragraph. The theoretical basis for "in high dimensions all samples look alike" is layed out in section 3.4 Instability Result of this paper.
Skipping the proof on page 6, essentially
... ... | Understand the illustration of the curse of dimensionality?
I think @Stephan Kolassa explains well what the authors meant in that paragraph. The theoretical basis for "in high dimensions all samples look alike" is layed out in section 3.4 Instability Result of |
38,284 | Understand the illustration of the curse of dimensionality? | imagine the unit cube $[0,1]^d$. All training data is sampled uniformly within this cube, i.e. $∀i,x_i∈[0,1]^d$, and we are considering the $k=10$ nearest neighbors of such a test point.
Let ℓ be the edge length of the smallest hyper-cube that contains all k-nearest neighbor of a test point. Then $ℓ^d≈\frac{k}{n}$ a... | Understand the illustration of the curse of dimensionality? | imagine the unit cube $[0,1]^d$. All training data is sampled uniformly within this cube, i.e. $∀i,x_i∈[0,1]^d$, and we are considering the $k=10$ nearest neighbors of such a test point.
Let ℓ be t | Understand the illustration of the curse of dimensionality?
imagine the unit cube $[0,1]^d$. All training data is sampled uniformly within this cube, i.e. $∀i,x_i∈[0,1]^d$, and we are considering the $k=10$ nearest neighbors of such a test point.
Let ℓ be the edge length of the smallest hyper-cube that contains all ... | Understand the illustration of the curse of dimensionality?
imagine the unit cube $[0,1]^d$. All training data is sampled uniformly within this cube, i.e. $∀i,x_i∈[0,1]^d$, and we are considering the $k=10$ nearest neighbors of such a test point.
Let ℓ be t |
38,285 | Forecasting Prices vs Returns by Deep Learning | What you've outlined is probably the single most common error that machine learning researchers make when analyzing financial data: it's trivial to discover that a great predictor of tomorrow's price is today's price.
The statistical term of art for this phenomenon is "non-stationarity." We have a number of questions a... | Forecasting Prices vs Returns by Deep Learning | What you've outlined is probably the single most common error that machine learning researchers make when analyzing financial data: it's trivial to discover that a great predictor of tomorrow's price | Forecasting Prices vs Returns by Deep Learning
What you've outlined is probably the single most common error that machine learning researchers make when analyzing financial data: it's trivial to discover that a great predictor of tomorrow's price is today's price.
The statistical term of art for this phenomenon is "non... | Forecasting Prices vs Returns by Deep Learning
What you've outlined is probably the single most common error that machine learning researchers make when analyzing financial data: it's trivial to discover that a great predictor of tomorrow's price |
38,286 | Forecasting Prices vs Returns by Deep Learning | The question: It seems that (univariate) forecasting stock market done
by websites using DL and LSTM actually does not work that well if we
focus on returns instead of prices. What is a relatively quick fix for
that?(or most important fix)
The motivation is quite simple. You can find it in any financial economic/econo... | Forecasting Prices vs Returns by Deep Learning | The question: It seems that (univariate) forecasting stock market done
by websites using DL and LSTM actually does not work that well if we
focus on returns instead of prices. What is a relatively qui | Forecasting Prices vs Returns by Deep Learning
The question: It seems that (univariate) forecasting stock market done
by websites using DL and LSTM actually does not work that well if we
focus on returns instead of prices. What is a relatively quick fix for
that?(or most important fix)
The motivation is quite simple. ... | Forecasting Prices vs Returns by Deep Learning
The question: It seems that (univariate) forecasting stock market done
by websites using DL and LSTM actually does not work that well if we
focus on returns instead of prices. What is a relatively qui |
38,287 | Forecasting Prices vs Returns by Deep Learning | Advances in Financial Machine Learning is a good reference for practical usage of ML in the context of financial time series.
Basically :
Formulating your label in term of level attained in a given amount of time (see chapter 3 barrier method) will help you build practical and realistic strategies. Unless you are doin... | Forecasting Prices vs Returns by Deep Learning | Advances in Financial Machine Learning is a good reference for practical usage of ML in the context of financial time series.
Basically :
Formulating your label in term of level attained in a given a | Forecasting Prices vs Returns by Deep Learning
Advances in Financial Machine Learning is a good reference for practical usage of ML in the context of financial time series.
Basically :
Formulating your label in term of level attained in a given amount of time (see chapter 3 barrier method) will help you build practica... | Forecasting Prices vs Returns by Deep Learning
Advances in Financial Machine Learning is a good reference for practical usage of ML in the context of financial time series.
Basically :
Formulating your label in term of level attained in a given a |
38,288 | What happens in the sub-areas of AI? (ML, DL) | This is just terminology, no need to think about it too much as different people classify different areas into different categories. For example a lot of statisticians would consider machine learning to be a sub-area of statistics, people from AI would consider machine learning to be a sub area of AI research, and peop... | What happens in the sub-areas of AI? (ML, DL) | This is just terminology, no need to think about it too much as different people classify different areas into different categories. For example a lot of statisticians would consider machine learning | What happens in the sub-areas of AI? (ML, DL)
This is just terminology, no need to think about it too much as different people classify different areas into different categories. For example a lot of statisticians would consider machine learning to be a sub-area of statistics, people from AI would consider machine lear... | What happens in the sub-areas of AI? (ML, DL)
This is just terminology, no need to think about it too much as different people classify different areas into different categories. For example a lot of statisticians would consider machine learning |
38,289 | What happens in the sub-areas of AI? (ML, DL) | Agreed with Karolis' answer in "there are no hard boundaries". In addition,
It's the same architecture of course. However, although we don't have a hard threshold on the number of layers for a neural network to be deep, in DL, we're more interested neural networks with large number of layers, instead of 1 or 2.
Typica... | What happens in the sub-areas of AI? (ML, DL) | Agreed with Karolis' answer in "there are no hard boundaries". In addition,
It's the same architecture of course. However, although we don't have a hard threshold on the number of layers for a neural | What happens in the sub-areas of AI? (ML, DL)
Agreed with Karolis' answer in "there are no hard boundaries". In addition,
It's the same architecture of course. However, although we don't have a hard threshold on the number of layers for a neural network to be deep, in DL, we're more interested neural networks with lar... | What happens in the sub-areas of AI? (ML, DL)
Agreed with Karolis' answer in "there are no hard boundaries". In addition,
It's the same architecture of course. However, although we don't have a hard threshold on the number of layers for a neural |
38,290 | What happens in the sub-areas of AI? (ML, DL) | I would second user2974951's comment (+1). Deep Learning entails stacking or layering within a methodology.
Points 1, 2 & 4 have been fully answered by Karolis (+1).
Regarding point 3: There are works that combined SVM and DNN. (e.g. Tang (2013) Deep Learning using Linear Support Vector Machines where it shows very p... | What happens in the sub-areas of AI? (ML, DL) | I would second user2974951's comment (+1). Deep Learning entails stacking or layering within a methodology.
Points 1, 2 & 4 have been fully answered by Karolis (+1).
Regarding point 3: There are wor | What happens in the sub-areas of AI? (ML, DL)
I would second user2974951's comment (+1). Deep Learning entails stacking or layering within a methodology.
Points 1, 2 & 4 have been fully answered by Karolis (+1).
Regarding point 3: There are works that combined SVM and DNN. (e.g. Tang (2013) Deep Learning using Linear... | What happens in the sub-areas of AI? (ML, DL)
I would second user2974951's comment (+1). Deep Learning entails stacking or layering within a methodology.
Points 1, 2 & 4 have been fully answered by Karolis (+1).
Regarding point 3: There are wor |
38,291 | Expected value until a success? | I would have liked to comment but I still can't... so I'll give a complete answer hoping that I'm not spoiling any homework.
I'd start saying that the "winning" sides of this five-sided unfair die are a distraction. We can re-arrange the calculations and obtain the expected value for a single roll:
$$ E_s = 0.9 \cdot \... | Expected value until a success? | I would have liked to comment but I still can't... so I'll give a complete answer hoping that I'm not spoiling any homework.
I'd start saying that the "winning" sides of this five-sided unfair die are | Expected value until a success?
I would have liked to comment but I still can't... so I'll give a complete answer hoping that I'm not spoiling any homework.
I'd start saying that the "winning" sides of this five-sided unfair die are a distraction. We can re-arrange the calculations and obtain the expected value for a s... | Expected value until a success?
I would have liked to comment but I still can't... so I'll give a complete answer hoping that I'm not spoiling any homework.
I'd start saying that the "winning" sides of this five-sided unfair die are |
38,292 | Expected value until a success? | Recursion
We can define the expected value of the game in a recursive manner. Let's assume that the expected benefit from the game (all rolls until the game ends) is x.
Using the probabilities you give for rolling {0.15,0.2,0.25,0.3,0.1} and the winnings of {\$1, \$2, \$3, \$4, [game ends]}, the expected value of the r... | Expected value until a success? | Recursion
We can define the expected value of the game in a recursive manner. Let's assume that the expected benefit from the game (all rolls until the game ends) is x.
Using the probabilities you giv | Expected value until a success?
Recursion
We can define the expected value of the game in a recursive manner. Let's assume that the expected benefit from the game (all rolls until the game ends) is x.
Using the probabilities you give for rolling {0.15,0.2,0.25,0.3,0.1} and the winnings of {\$1, \$2, \$3, \$4, [game end... | Expected value until a success?
Recursion
We can define the expected value of the game in a recursive manner. Let's assume that the expected benefit from the game (all rolls until the game ends) is x.
Using the probabilities you giv |
38,293 | Expected value until a success? | Simulation, per Comment: At each iteration, the die is rolled 400 times,
but only the rolls up to the first observed 5 are counted. Then
the payout w is computed, ignoring the trial on which the 5 occurred.
I chose 400 rolls because (with very high probability) that is enough
to get a 5. [Sloppy programming, wasteful o... | Expected value until a success? | Simulation, per Comment: At each iteration, the die is rolled 400 times,
but only the rolls up to the first observed 5 are counted. Then
the payout w is computed, ignoring the trial on which the 5 occ | Expected value until a success?
Simulation, per Comment: At each iteration, the die is rolled 400 times,
but only the rolls up to the first observed 5 are counted. Then
the payout w is computed, ignoring the trial on which the 5 occurred.
I chose 400 rolls because (with very high probability) that is enough
to get a 5.... | Expected value until a success?
Simulation, per Comment: At each iteration, the die is rolled 400 times,
but only the rolls up to the first observed 5 are counted. Then
the payout w is computed, ignoring the trial on which the 5 occ |
38,294 | Expected value until a success? | There's a .9 chance that you'll win 2.5 EV. You could also win a second 2.5. The probability of that, contingent on winning the first 2.5, is .9, for a total probability of .9^2. You can continue winning 2.5, with the probability of the nth win being .9^n. So you have
$\sum (2.5*.9^n) = 2.5\sum .9^n$, which is a geomet... | Expected value until a success? | There's a .9 chance that you'll win 2.5 EV. You could also win a second 2.5. The probability of that, contingent on winning the first 2.5, is .9, for a total probability of .9^2. You can continue winn | Expected value until a success?
There's a .9 chance that you'll win 2.5 EV. You could also win a second 2.5. The probability of that, contingent on winning the first 2.5, is .9, for a total probability of .9^2. You can continue winning 2.5, with the probability of the nth win being .9^n. So you have
$\sum (2.5*.9^n) = ... | Expected value until a success?
There's a .9 chance that you'll win 2.5 EV. You could also win a second 2.5. The probability of that, contingent on winning the first 2.5, is .9, for a total probability of .9^2. You can continue winn |
38,295 | Checking a beta regression model via glmmTMB with DHARMa package | tl;dr it's reasonable for you to worry, but having looked at a variety of different graphical diagnostics I don't think everything looks pretty much OK. My answer will illustrate a bunch of other ways to look at a glmmTMB fit - more involved/less convenient than DHARMa, but it's good to look at the fit as many differe... | Checking a beta regression model via glmmTMB with DHARMa package | tl;dr it's reasonable for you to worry, but having looked at a variety of different graphical diagnostics I don't think everything looks pretty much OK. My answer will illustrate a bunch of other way | Checking a beta regression model via glmmTMB with DHARMa package
tl;dr it's reasonable for you to worry, but having looked at a variety of different graphical diagnostics I don't think everything looks pretty much OK. My answer will illustrate a bunch of other ways to look at a glmmTMB fit - more involved/less conveni... | Checking a beta regression model via glmmTMB with DHARMa package
tl;dr it's reasonable for you to worry, but having looked at a variety of different graphical diagnostics I don't think everything looks pretty much OK. My answer will illustrate a bunch of other way |
38,296 | Checking a beta regression model via glmmTMB with DHARMa package | Have a look at the section about glmmTMB in the vignette of DHARMa. It seems to be an issue with regard to how predictions are calculated given the random effects.
As an alternative, you may try the GLMMadaptive package. You can find examples using the DHARMa here. | Checking a beta regression model via glmmTMB with DHARMa package | Have a look at the section about glmmTMB in the vignette of DHARMa. It seems to be an issue with regard to how predictions are calculated given the random effects.
As an alternative, you may try the G | Checking a beta regression model via glmmTMB with DHARMa package
Have a look at the section about glmmTMB in the vignette of DHARMa. It seems to be an issue with regard to how predictions are calculated given the random effects.
As an alternative, you may try the GLMMadaptive package. You can find examples using the DH... | Checking a beta regression model via glmmTMB with DHARMa package
Have a look at the section about glmmTMB in the vignette of DHARMa. It seems to be an issue with regard to how predictions are calculated given the random effects.
As an alternative, you may try the G |
38,297 | Checking a beta regression model via glmmTMB with DHARMa package | I am the developer of DHARMa. Dimitris and Ben are correct, the pattern originates from the known issue that glmmTMB does not (yet) allow making predictions based on fixed effects only, which sometimes produces this pattern. I hope we can fix this issue with the next release of glmmTMB, which should allow fixed-effect ... | Checking a beta regression model via glmmTMB with DHARMa package | I am the developer of DHARMa. Dimitris and Ben are correct, the pattern originates from the known issue that glmmTMB does not (yet) allow making predictions based on fixed effects only, which sometime | Checking a beta regression model via glmmTMB with DHARMa package
I am the developer of DHARMa. Dimitris and Ben are correct, the pattern originates from the known issue that glmmTMB does not (yet) allow making predictions based on fixed effects only, which sometimes produces this pattern. I hope we can fix this issue w... | Checking a beta regression model via glmmTMB with DHARMa package
I am the developer of DHARMa. Dimitris and Ben are correct, the pattern originates from the known issue that glmmTMB does not (yet) allow making predictions based on fixed effects only, which sometime |
38,298 | Should all adjustments be random effects in a mixed linear effect? | That is not right. A mixed effects model is a mixture of random effects and fixed effects. Generally, the point of adjusting for a random effect is to control for clustering indicators or combinations of covariates that are so high in dimension, the fixed effects would be unstable if not singular in a model. Random eff... | Should all adjustments be random effects in a mixed linear effect? | That is not right. A mixed effects model is a mixture of random effects and fixed effects. Generally, the point of adjusting for a random effect is to control for clustering indicators or combinations | Should all adjustments be random effects in a mixed linear effect?
That is not right. A mixed effects model is a mixture of random effects and fixed effects. Generally, the point of adjusting for a random effect is to control for clustering indicators or combinations of covariates that are so high in dimension, the fix... | Should all adjustments be random effects in a mixed linear effect?
That is not right. A mixed effects model is a mixture of random effects and fixed effects. Generally, the point of adjusting for a random effect is to control for clustering indicators or combinations |
38,299 | Should all adjustments be random effects in a mixed linear effect? | The first and foremost consideration that should drive the specification of random effects in a mixed effects model is the study design. Here are some examples that illustrate how the design affects the model specification.
Example 1
If you have a study in which you randomly selected patients from a target population ... | Should all adjustments be random effects in a mixed linear effect? | The first and foremost consideration that should drive the specification of random effects in a mixed effects model is the study design. Here are some examples that illustrate how the design affects t | Should all adjustments be random effects in a mixed linear effect?
The first and foremost consideration that should drive the specification of random effects in a mixed effects model is the study design. Here are some examples that illustrate how the design affects the model specification.
Example 1
If you have a stud... | Should all adjustments be random effects in a mixed linear effect?
The first and foremost consideration that should drive the specification of random effects in a mixed effects model is the study design. Here are some examples that illustrate how the design affects t |
38,300 | T-test paradox: can adding a single point very far from the null value change the outcome from significant to nonsignificant? | Perhaps I'm missing the gist of this question, but: if the next sample is really large variance will blow up, making your t-statistic smaller.
You can test it with made up data, for example
#Test if the average value of the sample c(2, 2.5, 3) is significantly different from zero
#> t.test(c(2,2.5,3))$p.value
#[1] 0.01... | T-test paradox: can adding a single point very far from the null value change the outcome from signi | Perhaps I'm missing the gist of this question, but: if the next sample is really large variance will blow up, making your t-statistic smaller.
You can test it with made up data, for example
#Test if t | T-test paradox: can adding a single point very far from the null value change the outcome from significant to nonsignificant?
Perhaps I'm missing the gist of this question, but: if the next sample is really large variance will blow up, making your t-statistic smaller.
You can test it with made up data, for example
#Tes... | T-test paradox: can adding a single point very far from the null value change the outcome from signi
Perhaps I'm missing the gist of this question, but: if the next sample is really large variance will blow up, making your t-statistic smaller.
You can test it with made up data, for example
#Test if t |
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