Delete pages

pages/Bonus.py

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-CURRENT_MODULE = "Bonus"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_01.py

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-CURRENT_MODULE = "Module_01"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_02.py

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-CURRENT_MODULE = "Module_02"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
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pages/Module_03.py

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-CURRENT_MODULE = "Module_03"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
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pages/Module_04.py

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-CURRENT_MODULE = "Module_04"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
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pages/Module_05.py

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-CURRENT_MODULE = "Module_05"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
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pages/Module_06.py

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-CURRENT_MODULE = "Module_06"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
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pages/Module_07.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_07"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_08.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_08"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_09.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_09"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_10.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_10"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_11.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_12.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_13.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_14.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_15.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_16.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_17.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_18.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_19.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Module_20.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Module_11"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/Overview.py

@@ -1,26 +0,0 @@
-CURRENT_MODULE = "Overview"
-  
-# Imports
-
-import streamlit as st
-import streamlit.components.v1 as components
-from   pages  import load_pages
-
-if "logged_in" not in st.session_state:
-   pass
-else:
-   load_pages.load_page(CURRENT_MODULE)
-   
-if __name__ == "__main__":
-   load_pages.test_load()
-   print("<<< Module [", CURRENT_MODULE, "] Compile Successful >>>")   
-
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pages/__init__.py

@@ -1,14 +0,0 @@
-
-if __name__ == "__main__":
-    print("<<< __init__ Compile Successful >>>")    
-   
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pages/assets/formulas.txt

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-Expander: Null Hypothesis
-**The “no‐effect” or “no‐difference” assumption**
-
-Mathematical Notation: 
-H₀: θ = θ₀ (e.g. H₀: μ = 0)
-
-*Example (one‐sample t‐test against μ₀ = 5):*
-*x = c(5.1, 4.9, 5.0, 5.2)*
-*t.test(x, mu = 5)*
-End Expander:
-Expander: Alternative Hypothesis
-**The different claim you wish to support**
-
-Mathematical Notation:
-– Two‐sided: Hₐ: θ ≠ θ₀
-– One‐sided: Hₐ: θ > θ₀  or  Hₐ: θ < θ₀
-
-*Example (test if mean > 5):*
-*t.test(x, mu = 5, alternative = "greater")*
-*or alternative = "two.sided" (default), "greater" or "less"*
-End Expander:
-Expander: p-value
-**Assuming H₀ is true, this is the probability of seeing data**
-**in your statistical test that is at least as extreme as yours**
-
-Mathematical Notation:
-p = P(Test statistic ≥ observed | H₀)  (one‐tailed)
-p = 2·P(|Test statistic| ≥ |observed| | H₀)  (two‐tailed)
-
-R function: 
-returned by many tests as $p.value
-
-*Example (from t.test above):*
-*res = t.test(x, mu = 5)*
-*res$p.value*
-End Expander:
-Expander: Confidence Interval
-A range of plausible values for the parameter calculated so that 
-e.g. 95% of such intervals (in repeated sampling) contain the true value
-Mathematical form & notation:
-CI₁₋α = [θ̂ ± z₁₋α/₂·SE(θ̂)]         for normal distribution
-CI₁₋α = [θ̂ ± t_{n–1,1–α/₂}·SE(θ̂)]  for an unknown σ
-
-*Example (95% CI for mean):*
-*t.test(x, conf.level = 0.95)$conf.int*
-End Expander:
-Expander: Significance Level (α) 
-**The cutoff probability for a rare result under H₀**
-**if p ≤ α you reject H₀**
-
-Mathematical Notation: 
-α = P(Type I error) 
-= P(reject H₀ | H₀ true)
-
-R function: 
-you compare to see if p.value ≤ α 
-and conf.level = 1–α
-End Expander:
-Expander: One-tailed test
-**A test that looks for an effect in only one direction (greater or less)**
-
-Mathematical Notation: 
-Hₐ: θ > θ₀ or Hₐ: θ < θ₀
-
-R function: 
-alternative = "greater" or "less"
-
-*Example:*
-*t.test(x, mu = 5, alternative = "less")*
-End Expander:
-Expander: Two-tailed Test
-**A test that looks for an effect in either direction**
-
-Mathematical Notation: 
-Hₐ: θ ≠ θ₀
-
-R function: 
-alternative = "two.sided" (default)
-
-*Example:*
-*t.test(x, mu = 5, alternative = "two.sided")*
-End Expander:
-Expander: Normal Distribution
-**The symmetric bell‐shaped distribution**
-Many statistical curves “look normal” when the sample size is large
-
-Mathematical Notation: 
-X ~ N(μ, σ²)
-The amount of area under the normal curve when 2 standard deviations
-away to the right is written as P(Z ≤ 2.0) = 97.72% of the curve
-
-R functions:
-– dnorm(x, mean, sd)  density
-– pnorm(q, mean, sd)  pdf
-– qnorm(p, mean, sd)  inverse of pnorm
-– rnorm(n, mean, sd)  n random samples
-
-*Example:*
-*pnorm(2.0,    mean = 0, sd = 1)       # = 97.72%*
-*qnorm(0.9772, mean = 0, sd = 1)       # = 2.0 = inverse of p*
-End Expander:
-Expander: False Positive (Type I Error)
-**Rejecting H₀ when it is actually true**
-*e.g. Unicorns do not exist but the statistical test said they did*
-*when the null hypothesis (H₀) or base assumption is unicorns do not exist*
-Mathematical Notation: 
-P(Type I error) = α
-End Expander:
-Expander: False Negative (Type II error)
-**Failing to reject H₀ when it is actually false**
-*e.g. Unicorns exist but the statistical test said they do not*
-*when the null hypothesis (H₀) or base assumption is unicorns do not exist*
-Also called Beta(β)
-
-Mathematical Notation:
-β = P(Type II error)
-End Expander:
-Expander: Power
-Power is defined as 1 - Beta (β)
-Reminder: Beta is a False Negative (Type II error)
-so Beta is Failing to reject H₀ when it is actually false
-
-Mathematical Notation: 
-Power = 1 – β 
-Power = P(reject H₀ | Hₐ true)
-the conditional probability of rejecting the null hypothesis
-when the alternative hypothesis is true and the null is false
-
-R function: 
-use power.t.test(), power.prop.test(), etc.
-
-*Example (power for one‐sample t):*
-*power.t.test(n = 20, delta = 1, sd = 2, sig.level = 0.05, type = "one.sample")*
-End Expander:
-Expander: One-Sample T-Test
-**Tests whether the mean of one group differs from a given value μ₀**
-
-Mathematical Notation:
-t = (x̄ – μ₀) / (s / √n),   df = n – 1
-
-R function: 
-t.test(x, mu, alternative, conf.level) 
-*where:*
-– x: numeric vector
-– mu: hypothesized mean μ₀
-– alternative: "two.sided"/"greater"/"less"
-– conf.level: e.g. 0.95
-
-*Example:*
-*x = c(5.1, 4.9, 5.0, 5.2)*
-*t.test(x, mu = 5, alternative = "two.sided", conf.level = 0.95)*
-End Expander:
-Expander: Two-Sample T-Test
-**Tests whether two group means differ.**
-
-Mathematical Notation (unequal variances):
-t = (x̄₁ – x̄₂) / √(s₁²/n₁ + s₂²/n₂),  df by Welch’s formula
-
-R function: 
-t.test(x, y, var.equal = FALSE, alternative, conf.level) 
-*where:*
-– x, y: numeric vectors for each group
-– var.equal: TRUE for pooled‐variance test, FALSE for Welch’s
-
-*Example:*
-*groupA = c(5.1,4.9,5.0,5.2)*
-*groupB = c(5.3,5.4,5.2,5.5)*
-*t.test(groupA, groupB, var.equal = FALSE)*
-End Expander:
-Expander: Z-test
-**Like a t-test but uses a known σ or large-n approximation**
-
-Mathematical Notation:
-z = (x̄ – μ₀) / (σ / √n),   compare to N(0,1)
-
-R function: 
-use BSDA::z.test() or do manually (below)
-
-*Example*
-*x = c(102, 98, 100, 101, 99)*
-*n = length(x)*
-*mu0 = 100*
-*sigma = 2*
-*z = (mean(x) - mu0) / (sigma / sqrt(n))*
-*p_value = 2 * pnorm(-abs(z))  # two‐sided*
-End Expander:
-Expander: Chi-Squared Test of Independence
-**Tests whether two Categorical variables are related**
-
-Mathematical Notation:
-χ² = Σ_{i,j} (O_{i,j} – E_{i,j})² / E_{i,j},   df = (rows–1)(cols–1)
-
-R function: 
-chisq.test(x, correct = TRUE/FALSE) 
-*where:*
-– x: contingency table (matrix or table)
-– correct: apply Yates’s continuity correction for 2×2 tables
-
-*Example:*
-*tbl = matrix(c(12, 5,7, 16), nrow = 2,*
-*              dimnames = list(Gene = c("A","B"),*
-*                              Outcome = c("Yes","No")))*
-*chisq.test(tbl, correct = FALSE)*
-End Expander:
-Expander: Chi-Squared Goodness-of-Fit Test (Frequencies)
-**Tests if observed sample counts match expected proportions**
-
-Mathematical Notation:
-χ² = Σ (O_i – E_i)² / E_i,   df = k – 1
-
-R function: 
-chisq.test(x, p) 
-*where:*
-– x: vector of observed counts
-– p: vector of hypothesized probabilities (sum to 1)
-
-*Example:*
-*observed       = c(50, 30, 20)*
-*expected_probs = c(0.5, 0.3, 0.2)*
-*chisq.test(observed, p = expected_probs)*
-End Expander:

pages/assets/profile.txt

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-# 🚀 Student Profile
-
-- **Student Name:** Charlie B
-- **Department:** BMO Wealth
-- **Expertise:** Intermediate
-
-## 🎯 Interests:
-- Data science and analytics
-- Predictive models
-- Capital market applications
-
-## 🎯 Goals for Next Review:
-- Complete topics in **Supervised Learning**.
-- Begin study on Advanced Optimization Methods.
-- Initiate **Computer Vision** topics.
-
-** Keep up the excellent work **

pages/assets/progress.txt

@@ -1,43 +0,0 @@
-# 🚀 Progress Report
-
-- **Student Name:** <Your Name>
-- **Enrollment Date:** Sept 15, 2025
-- **Report Date:** Nov 2, 2025
-
----
-
-# 📚 Progress Overview
-Completed topics are marked with a checkmark (✔️), 
-while incomplete or pending topics remain unchecked.
-
-## 📈 Overall Progress Summary
-
-| Curriculum Area              | Completion (%) |
-|------------------------------|----------------|
-| Overview                     | 100%           |
-| Inference Concepts           | 100%           |
-| One and Two-Tailed Tests     | 100%           |
-| Mathematical Concepts        | 75%            |
-| Normal Distribution          | 75%            |
-| T-tests                      | 70%            |
-| Paired T-tests               | 65%            |
-| Chi-Squared Independence     | 0%             |
-| Chi-Squared Frequencies      | 0%             |
-| Summary                      | 0%             |
-
----
-
-## ✅ Statistics and Probability
-| Subtopic                               | Status  |
-|----------------------------------------|---------|
-| Measures of Central Tendency           | ✔️      |
-| Variance and Standard Deviation        | ✔️      |
-| Probability Distributions              | ✔️      |
-| Central Limit Theorem & Applications   | ✔️      |
-| Hypothesis Testing                     | ✔️      |
-| Bayesian Statistics                    |         |
-
----
-
-**Keep up the excellent work, you're well on your way** 
-**to mastering this module! 🚀**

pages/load_pages.py

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-
-# =========================================================================
-# Imports
-# =========================================================================
-
-import os, time, re, sys
-import numpy as np
-from   openai import OpenAI
-
-import streamlit as st
-import streamlit.components.v1 as components
-
-import urllib.parse
-
-# =========================================================================
-# AI Response Dialog
-# =========================================================================
-
-@st.dialog("🤖 Response")
-def AI_gen_response(prompt):
-
-   MIN_PROMPT_LENGTH = 6
-
-   if len(prompt.strip()) < MIN_PROMPT_LENGTH:
-      print("ERROR: AI prompt too short")
-      st.markdown("*Your input is too short, please retype ...*")      # clear output and print statement in italics
-      return   
-
-   # AI API Call
-
-   with st.spinner("Thinking ...", show_time=True):
-
-      st.markdown("*Thinking about your response ...*")      # clear output and print statement in italics
-
-      full_text = []
-      client    = st.session_state["openai_client"]
-
-      stream = client.chat.completions.create(
-               model    = st.session_state["openai_model"],
-               messages = [{"role": "user",  "content": prompt}],        # {"role": "admin", "content": admin_prompt},                 
-               stream    = True,
-      )
-
-      for chunk in stream:                                # chunk is a ChatCompletionChunk
-        delta = chunk.choices[0].delta                    # “what changed since the prev. chunk”
-        if delta.content:                                 # sometimes None (e.g. first chunk)
-           print(delta.content, end="", flush=True)       # live printing
-           full_text.append(delta.content)                # keep it if you need it
-           response = "".join(full_text)                  # the entire response as one string
-
-      format_text(response, input_directory="")           # there is no input directory for the OpenAI API
-
-   return response                                        # return the response for more programming if needed
-
-
-# =========================================================================
-# Symbolic Lookup Table - Greek (lower and upper, math and equality signs)
-# =========================================================================
-
-SYMBOLS: dict[str, str] = {
-    r"\alpha": "α",  r"\beta": "β",   r"\gamma": "γ",   r"\delta": "δ",r"\epsilon": "ε",r"\zeta": "ζ",   r"\eta": "η",     r"\theta": "θ",  r"\iota": "ι",   r"\kappa": "κ",  r"\lambda": "λ",  r"\mu": "μ", r"\nu": "ν",     r"\xi": "ξ",     r"\omicron": "ο", r"\pi": "π",  r"\rho": "ρ",    r"\sigma": "σ",  r"\tau": "τ",     r"\upsilon": "υ", r"\phi": "φ",    r"\chi": "χ",    r"\psi": "ψ",     r"\omega": "ω",
-    r"\Alpha": "Α",  r"\Beta": "Β",   r"\Gamma": "Γ",   r"\Delta": "Δ", r"\Epsilon": "Ε",r"\Zeta": "Ζ",   r"\Eta": "Η",     r"\Theta": "Θ",  r"\Iota": "Ι",   r"\Kappa": "Κ",  r"\Lambda": "Λ",  r"\Mu": "Μ", r"\Nu": "Ν",     r"\Xi": "Ξ",     r"\Omicron": "Ο", r"\Pi": "Π", r"\Rho": "Ρ",    r"\Sigma": "Σ",  r"\Tau": "Τ",     r"\Upsilon": "Υ",     r"\Phi": "Φ",    r"\Chi": "Χ",    r"\Psi": "Ψ",     r"\Omega": "Ω",
-    r"\pm": "±",      r"\times": "×",   r"\div": "÷",        r"\cdot": "·",     r"\sqrt": "√",    r"\sum": "∑",     r"\prod": "∏",       r"\int": "∫", r"\infty": "∞",   r"\partial": "∂", r"\nabla": "∇",     r"\le": "≤",      r"\ge": "≥",      r"\neq": "≠",        r"\approx": "≈",
-    r"\and": "∧",     r"\or": "∨",      r"\neg": "¬",     r"\in": "∈",      r"\notin": "∉",  r"\subset": "⊂",  r"\subseteq": "⊆",r"\supset": "⊃",     r"\supseteq": "⊇",     r"\cup": "∪",     r"\cap": "∩",     r"\angle": "∠",   r"\triangle": "△",
-    r"\therefore": "∴", r"\because": "∵",     r"\propto": "∝",  r"\parallel": "∥",r"\perp": "⊥", r"\degree": "°",  r"\bullet": "•",     r"\rightarrow": "→", r"\leftarrow": "←", r"\uparrow": "↑",
-    r"\downarrow": "↓", r"\leftrightarrow": "↔",     r"\ell": "ℓ",     r"\Re": "ℜ",      r"\Im": "ℑ",     r"\aleph": "ℵ",   r"\wp": "℘"
-}
-
-# =====================================================
-# Functions - to convert symbol tags to symbols
-# =====================================================
-
-FORMULA_REGEX  = re.compile(r'\\[A-Za-z]+')
-BOLD_TXT_REGEX = re.compile(r'\\[A-Za-z]+{([^{}]*)}')
-
-def test_load():
-   # print("load_pages - testing external call")          # DEBUG
-   pass
-
-def latex_to_unicode(text: str, table: dict[str, str] = SYMBOLS) -> str:
-   return FORMULA_REGEX.sub(lambda m: table.get(m.group(0), m.group(0)), text)
-
-def latex_bold_to_markdown(text: str) -> str:
-    return BOLD_TXT_REGEX.sub(r'**\1**', text)
-
-# =====================================================================================
-# format_text - loads a text file, formats it and places streamlit components
-#               based on instructions from the text file (lesson/learning module) file
-# =====================================================================================
-
-def format_text(input_text, input_directory=""):
-
-   # --------------------------------------------------------------------------------
-   # Uses text file as input - input_text
-   # within the text are instructional tags used to build the content for the page
-   # This code parses the text line-by-line to determine what to display for the page
-   # --------------------------------------------------------------------------------
-
-   # Parse the raw text for certain backslashes for mathematical formulae and
-   # instruct the program to call the st.latex function to render
-
-   input_text = input_text.replace("\\[\n",'st.latex("')
-   input_text = input_text.replace("\\[",'st.latex("')
-   input_text = input_text.replace("\n\\]\n",'")\n')
-   input_text = input_text.replace("\\]\n",'")\n')
-
-   # Split the text line by line to parse
-
-   lines = input_text.split("\n")
-   idx   = 0
-
-   # Scan each line
-
-   while(idx<len(lines)):
-      
-      line = lines[idx].strip()
-
-      # -------------------------------
-      # Single-line interface tags
-      # -------------------------------
-
-      # Latex expression - render using st.latex()
-
-      if line[:len("st.latex(")]=="st.latex(":
-         if line[-2:]=='")': 
-            st.latex(line[len("st.latex(")+1:-2])    # closed expression
-         else:
-            st.latex(line[len("st.latex(")+1:])      # open expression
-
-      elif line[:2]=='")':                           # closed expression notation - ignore 
-         pass
-
-      # Question tag
-
-      elif line[:len("Question: ")]=="Question: ":
-         #st.text_area(label, value="", height=None, max_chars=None, key=None, help=None, on_change=None, args=None, kwargs=None, *, placeholder=None, disabled=False, label_visibility="visible")
-         with st.expander(label = line[len("Question: [00_01_02]")+1:] + "...", expanded=False, icon=":material/live_help:"):
-            question_key = line[11:19]   #  e.g. Question: [14_01_02]
-            print(line, question_key)
-            answer_text  = st.text_area(label="Answer to Question", value="", height=200, key=question_key)
-
-      # Hint tag
-
-      elif line[:len("Hint: ")]=="Hint: ":
-         Hint_text = line.split("...")
-         with st.expander(label = Hint_text[0]+"...", expanded=False, icon=":material/voice_selection:"):
-            st.markdown(Hint_text[1])
-
-      # Load image tag - image is located in the module content folder and the tag has the file path
-
-      elif line[:len("Image: ")]=="Image: ":
-         file_str = line[len("Image: "):].strip()
-         default_width = 700
-         if file_str[-1]=="]":                       # a picture width (optional) is given
-            default_width = int(file_str[file_str.find("[")+1:-1])
-            file_str = file_str[:file_str.find("[")]
-         st.image(file_str, width=default_width)     # st.image(image, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto", *, use_container_width=False)
-
-      # iFrame tag - render a web site in a container within a tab
-
-      elif line[:len("iframe: ")]=="iframe: ":
-         print("Running iframe")
-         file_str = line[len("iframe: "):].strip()   # Get web site
-         file_str = file_str[1:-1]                   # Remove string delimiters 
-         print("Loading iframe:",file_str)
-         components.iframe(file_str,500,500)
-
-      # Link to sample code hosted externally e.g. Google Colab
-
-      elif line[:len("Code: ")]=="Code: ":           
-         file_str = line[len("Code: "):].strip()
-         with st.expander(label = "Code for this lesson ...", expanded=True, icon=":material/terminal:"):
-            st.markdown(file_str)
-
-      # HTML instructions rendered inside a frame e.g. an interactive javascript element
-      # the HTML page is stored in the module content directory or is loaded from external source
-
-      elif line[:len("HTML: ")]=="HTML: ":           # An HTML page to load and display - eg Web Widget
-
-         default_width  = 700
-         default_height = 700
-
-         # print("HTML ",input_directory)  # DEBUG
-
-         file_str = line[len("HTML: "):].strip()
-
-         if file_str[-1]=="]":                                          # an optional video width is given
-            default_width  = int(file_str[file_str.find("[")+1:-1])
-            default_height = default_width                              # square by default
-            file_str = file_str[:file_str.find("[")]
-
-         if file_str[:4] != "http":
-            file_str = os.path.join(input_directory,file_str)
-
-         # print(file_str)  # DEBUG
-
-         try:
-            html_str = open(file_str, "r", encoding="UTF-8").read()
-         except Exception as err:
-            print(f"Unexpected {err=}, {type(err)=}",file_str)            
-         else:
-            components.html(html_str, height=default_height, width=default_width, scrolling=True)
-
-      # External Web Link
-
-      elif line[:len("Link: ")]=="Link: ":
-         file_str = line[len("Link: "):].strip()
-         st.markdown(file_str)
-
-      # Text that can be copied
-
-      elif line[:len("Copy: ")]=="Copy: ":
-         file_str = line[len("Copy: "):].strip()
-         st.code(file_str,language="")
-
-      # Video
-
-      elif line[:len("Video: ")]=="Video: ":
-
-         default_width  = 700
-
-         file_str = line[len("Video: "):].strip()
-
-         if file_str[-1]=="]":                                          # an optional video width is given
-            default_width = int(file_str[file_str.find("[")+1:-1])
-            file_str = file_str[:file_str.find("[")]
-         
-         video_filepath = os.path.join(st.session_state["start_dir"],"pages",input_directory,file_str)
-         video_file     = open(video_filepath,"rb")
-
-         if video_file is None:
-            print("ERROR: Video did not load: ",video_filepath)
-         else: 
-            intro_video    = video_file.read()
-            st.video(intro_video, loop=True, autoplay=True, muted=False)   # width=default_width) in doc v1.4.6 (reload ??)
-
-
-      # ---------------------------------------------------------------------
-      # Multi-line interface tags
-      #   These tags tell the program to scan ahead and read lines until a
-      #   closure tag is found.  The line pointer is advanced accordingly
-      # ---------------------------------------------------------------------
-
-      # R Code is run in an embedded widget.  This will prepopluate the widget with the example code
-
-      elif line[:len("Expander:")]=="Expander:":
-
-         default_width  = 800
-
-         expander_headline = line[len("Expander:"):].strip()
-         expander_string   = ""
-
-         for c, expander_line in enumerate(lines[idx+1:]):
-            if expander_line[:len("End Expander:")]=="End Expander:":
-               idx+=1    # skip this line
-               break
-            else:
-               expander_string += expander_line + "\n"
-
-         idx+=c          # Set global counter to skip past the lines already read in lookahead mode    
-
-         with st.expander(label = expander_headline+"...", expanded=False, icon=":material/stars_2:"): # ,width=default_width):
-            for expander_line in expander_string.split("\n"):
-               #st.write(expander_line)
-               st.markdown(expander_line)
-
-      # R Code is run in an embedded widget.  This will prepopluate the widget with the example code
-
-      elif line[:len("R Code:")]=="R Code:":
-
-         r_code_string = ""
-
-         for c, r_code_line in enumerate(lines[idx+1:]):
-            if r_code_line[:len("End R Code:")]=="End R Code:":
-               idx+=1    # skip this line
-               break
-            else:
-               r_code_string += r_code_line + "\n"
-
-         idx+=c          # Set global counter to skip past the lines already read in lookahead mode    
-         
-         URI_code = urllib.parse.quote(r_code_string)
-         components.html("<iframe width='675' height='575' src='https://rdrr.io/snippets/embed/?code=" + URI_code + "' frameborder='0'></iframe>", height=600, width = 700, scrolling=True)
-
-      # ---------------------------------------------------------------------
-      # A Cognitive Checkpoint is a point in the module where the student is asked to 
-      # write a briefing that demonstrates whether they understand a concept
-      # The briefing is sent to the AI for evaluation
-      # Constructing this as a multi-line input gives the ability to enhance the admin prompt
-      # which will need custom instructions based on the checkpoint
-      # ---------------------------------------------------------------------
-
-      elif line[:len("Cognitive Checkpoint:")]=="Cognitive Checkpoint:":
-
-         question_key     = line[23:31]     # Unique key for response
-         question         = ""              # Multi-line question for cognitive response
-
-         for c, question_line in enumerate(lines[idx+1:]):
-            if question_line[:len("End Cognitive Checkpoint:")]=="End Cognitive Checkpoint:":
-               idx+=1    # skip this line
-               break
-            else:
-               question += question_line + "\n"
-
-         idx+=c                             # Set global counter to skip past the lines already read in lookahead mode
-
-         st.write(question)                 # Write question               
-
-         # Provide area to write response
-
-         with st.expander(label = "Cognitive Checkpoint:", expanded=True, icon=":material/live_help:"):
-            
-            if answer_text := st.text_area(label="Answer to Question", value="", height=150, key=question_key):          # := is the "Walrus Operator - assigns, checks and runs function"
-
-               admin_prompt = '''
-               You are a teacher.
-               Evaluate the following statement delimited in square brackets for accuracy from your student.
-               For each logical statement, grade it as one of three options: correct, misunderstanding, incorrect.
-               For each statement cite the grade, student comment, your response, corrected response if required.
-               Provide a summary at the end, not in table form, with the grade and a summary of the student comment
-               '''
-
-               # Return your assessment in JSON format   # Optional - for eval purposes
-         
-               AI_question = admin_prompt + "[" + answer_text + "]"
-
-               try:
-                  AI_gen_response(prompt=AI_question)
-               except Exception as e:
-                  print("ERROR: Dialog box open.  Message suppressed\n",str(e))
-               
-
-      # ------------------------------------------
-      # DEFAULT Instructions if no tag is provided
-      # ------------------------------------------
- 
-      else:
-         line_unicode = latex_to_unicode(line)                 # convert text to unicode symbols if needed
-         line_bold    = latex_bold_to_markdown(line_unicode)   # bold text
-         # print(line_bold)
-         st.markdown(line_bold)                                # markdown that line
-        
-      # ------------------------------------------
-      # Increment line counter / go to next line
-      # ------------------------------------------
-      
-      idx+=1
-
-
-      # END Parse text
-
-   return
-
-
-# =====================================================
-# Load Content
-# =====================================================
-
-def load_content(module):
-
-   input_directory = os.path.join(st.session_state["start_dir"],"pages","modules",module)
-
-   # if file exists - otherwise do nothing
-   content_str     = open(os.path.join(input_directory,"Overview.txt"), "r", encoding="UTF-8").read()
-   tab_content     = content_str.split("---\n")
-
-   # Display Module title if one is given, otherwise blank
-
-   title = tab_content[0].strip()
-   if len(title)>0:
-      st.subheader(title)
-
-   # Draw tabs according to page count   
-
-   tab_list = ["🚀"+str(idx+1) for idx,x in enumerate(tab_content[1:])]
-   tabs     = st.tabs(tab_list)
-
-   # For each tab, render the appropriate content
-
-   for idx,tab in enumerate(tabs):
-      with tab:
-         format_text(tab_content[idx+1], input_directory)
-
-   # Add AI Bot Interface to bottom of page        
-
-##   if prompt := st.chat_input("I would like to learn about ..."):
-##      AI_gen_response(prompt=prompt)
-
-   return
-
-# =====================================================
-# Load Page
-# =====================================================
-
-def load_page(module):
-
-   if "logged_in" not in st.session_state:                       # User is not logged in - don't load content
-      return
-
-   # --------------------------------------------------
-   # Sidebar
-   # --------------------------------------------------
-
-   asset_dir = os.path.join(st.session_state["start_dir"],"pages","assets")
-
-   with st.sidebar:
-
-      with st.expander("Lessons", expanded=True, icon=":material/library_books:"):    
-
-         st.page_link("pages/Overview.py",  label="The ASCEND User Interface",             icon=":material/rocket_launch:")
-         st.page_link("pages/Module_01.py", label="Basic Training",                        icon=":material/school:")
-         st.page_link("pages/Module_02.py", label="Mathematical Foundations",              icon=":material/difference:")
-         st.page_link("pages/Module_03.py", label="The Normal Distribution",               icon=":material/cadence:")
-         st.page_link("pages/Module_04.py", label="One Sided Tests",                       icon=":material/compare:")
-         st.page_link("pages/Module_05.py", label="Tests for Two Samples",                 icon=":material/speed_2x:")  
-         st.page_link("pages/Module_06.py", label="Paired Tests",                          icon=":material/speed_2x:")
-         st.page_link("pages/Module_07.py", label="Testing Independence",                  icon=":material/superscript:")
-         st.page_link("pages/Module_08.py", label="Testing Frequencies",                   icon=":material/superscript:")
-         st.page_link("pages/Module_09.py", label="Summary",                               icon=":material/format_list_numbered:")
-         st.page_link("pages/Bonus.py",     label="Bonus Content",                         icon="🎁")
-
-      with st.expander("Ask the AI", expanded=False, icon=":material/smart_toy:"):
-
-         if AI_question := st.text_input(label="Ask the AI", value="", key="AI_Question"):     
-            try:
-               AI_gen_response(prompt=AI_question)
-            except Exception as e:
-               print("ERROR: Dialog box open.  Message suppressed\n",str(e))
-
-         # Callback function to clear text from previous AI question inputs
-
-         def set_name(new_text):
-            st.session_state.AI_Question = new_text
-
-         st.button('Clear',on_click=set_name, args=[''])
-
-      with st.expander("R Calculator", expanded=False, icon=":material/calculate:"):
-         components.html("<iframe width='500' height='500' src='https://rdrr.io/snippets/embed/?code=""' frameborder='0'></iframe>", height=550, width = 550, scrolling=True)
-
-      # Streamlit 1.47 is required to run expanders within expanders 
-      #with st.expander("Statistical Tests", expanded=False, icon=":material/functions:"):
-      #   overview_dir = os.path.join(st.session_state["start_dir"],"pages","assets")
-      #   overview_str = open(os.path.join(overview_dir,"formulas.txt"), "r", encoding="UTF-8").read()
-      #   format_text(overview_str, overview_dir)         
-
-      with st.expander("Your Progress",expanded=False, icon=":material/preliminary:"):
-         progress_str = open(os.path.join(st.session_state["start_dir"],"pages","assets","progress.txt"), "r", encoding="UTF-8").read()
-         st.markdown(progress_str)
-
-      with st.expander("Logout", expanded=False, icon=":material/logout:"):      # 
-         if st.button("Logout"):
-            del st.session_state["logged_in"]
-            st.switch_page("app.py")                               # switch to main page
-            st.rerun()                                             # should invoke login dialog
-
-   # END Sidebar
-
-   # --------------------------------------------------
-   # Main Screen Content - e.g. Pages with Tabs
-   # --------------------------------------------------
-
-   if "logged_in" not in st.session_state:                         # check if user has logged out with sidebar
-      pass
-   else:
-      load_content(module)                                         # otherwise load and provide access to the content
-
-# =================================================
-# Standalone Code if Program is Run as Main
-# =================================================   
-
-if __name__ == "__main__":
-   pass
-

pages/modules/Bonus/Overview - Backup.txt

@@ -1,43 +0,0 @@
- 
----
-
-R Code:
-
-set.seed(42)
-
-# 1. Simulate rolls for Set A (fair dice) and Set B (slightly high-biased dice)
-N <- 1000
-# Fair dice: equal probs 1–6
-rolls_A <- replicate(N, sum(sample(1:6, 2, replace=TRUE)))
-# Biased dice: face “6” is more likely
-p_biased <- c(1,1,1,1,1,5)/10
-rolls_B <- replicate(N, sum(sample(1:6, 2, replace=TRUE, prob=p_biased)))
-
-# 2. Summarize
-mean_A <- mean(rolls_A); sd_A <- sd(rolls_A)
-mean_B <- mean(rolls_B); sd_B <- sd(rolls_B)
-cat("Set A: mean =", round(mean_A,3),  " sd =", round(sd_A,3), "\n",
-    "Set B: mean =", round(mean_B,3),  " sd =", round(sd_B,3), "\n\n")
-
-# 3. Two-sample Welch t-test
-test_two_sample <- t.test(rolls_A, rolls_B)
-print(test_two_sample)
-
-# 4. Visualize: overlaid histograms
-hist(rolls_A, freq=FALSE, col=rgb(0,0,1,0.4),
-     xlim=c(2,12), ylim=c(0,0.35),
-     main="Fair (blue) vs Biased (red) Dice Sums",
-     xlab="Sum of Two Dice", ylab="Density")
-hist(rolls_B, freq=FALSE, col=rgb(1,0,0,0.4), add=TRUE)
-legend("topright",
-       legend=c("Set A (fair)", "Set B (biased)"),
-       fill=c(rgb(0,0,1,0.4), rgb(1,0,0,0.4)))
-
-End R Code:
-
----
-iframe: "https://theplayroom.babylonjs.com/"
----
-iframe: "https://time-loop.fr/Arques/EXPERIENCES/3DSCAN/#!/"
----
-iframe: "https://www.babylonjs.com/viewer.html?url=https://babylonjsassetslib.z01.azurefd.net/DEMOS/demos/monstera_deliciosa_potted_mid-century_plant.glb"

pages/modules/Bonus/Overview.txt

@@ -1,108 +0,0 @@
- 
----
-##  Navigational Map
-#### Click Start> below to navigate Statistical Inference tests and when to use them
-HTML: navigational_map.html[1100]
----
-Image: pages/modules/Bonus/checkpoint_banner.jpg
-
-Cognitive Checkpoint: [00-00-00]
-- Describe what statistical inference is and how it is applied 
-- Using the example of "Do unicorns exist", walkthrough the steps for a statistical inference test
-- When would my test be statistically significant ?
-- If statistically significant, would this prove unicorns exist and why?
-End Cognitive Checkpoint:
----
-R Code:
-
-trial_sizes <- c(10, 100, 1000, 10000)
-
-# Prepare 2×2 plotting area
-par(mfrow = c(2, 2),
-    mar   = c(4, 4, 2, 1) + 0.1)  # adjust margins: bottom, left, top, right
-
-# Loop over each sample size
-for (n_trials in trial_sizes) {
-  
-  # Simulate sums of two fair dice
-  roll_sums <- replicate(n_trials,
-                         sum(sample(1:6, size = 2, replace = TRUE)))
-  
-  # Estimate mean and sd
-  mu_hat    <- mean(roll_sums)
-  sigma_hat <- sd(roll_sums)
-  
-  # Plot histogram scaled to density
-  hist(roll_sums,
-       breaks      = seq(1.5, 12.5, by = 1),
-       probability = TRUE,
-       col         = "lightblue",
-       border      = "white",
-       xlim        = c(2, 12),
-       ylim        = c(0, 0.30),
-       xlab        = "Sum of Two Dice",
-       main        = paste("n =", n_trials))
-  
-  # Overlay Normal curve with estimated parameters
-  x_vals <- seq(2, 12, length.out = 200)
-  y_vals <- dnorm(x_vals, mean = mu_hat, sd = sigma_hat)
-  lines(x_vals, y_vals, col = "red", lwd = 2)
-  
-  # Add legend
-  legend("topright",
-         legend = sprintf("N(%.2f, %.2f²)", mu_hat, sigma_hat),
-         lty    = 1,
-         col    = "red",
-         bty    = "n")
-}
-
-# Reset plotting parameters if needed
-par(mfrow = c(1,1))
-
-End R Code:
-
----
-#### Test yourself using the flashcards
-HTML: flashcards.html
-
----
-#### Statistical Inference Walkthrough
-
-Expander: Frame Your Question as Two Competing Stories
-• Base story (“null hypothesis”): The dice are fair
-• Alternative story: The dice are biased so low or high sums occur more often than with fair dice
-End Expander:
-Expander: Gather Your Evidence
-• Decide how many times you will roll the dice.
-• Keep track of the outcome (the sum) for each roll.
-End Expander:
-Expander: Summarize What You See
-• Organize your recorded sums in a simple way (e.g count how often each sum appears)
-• Look for patterns — do certain sums show up more or less often?
-End Expander:
-Expander: Compare to What You’d Expect If the Dice Were Fair
-• Ask yourself: “Do the patterns I see match what typically happens with fair dice?”
-• Remember even fair dice will show high or low counts in a limited set of rolls
-End Expander:
-Expander: Judge whether the Differences Are Random or Suggest Bias
-• Small fluctuations are normal when you roll only a few times
-• Very large or consistent departures from the fair-dice pattern might indicate bias
-End Expander:
-Expander: Reach a Conclusion
-• If your observed pattern looks fair dice allowing for normal ups and downs, you observe the dice are fair
-• If the pattern is so skewed it seems unlikely, you go with the belief the dice are biased
-End Expander:
-Expander: Summary of statistical inference
-• Starting with a clear question (fair vs. biased dice).
-• Collecting and summarizing real-world data (rolls and sums).
-• Judging how well that data lines up with the “fair dice” story.
-• Making a reasoned decision whether the dice are fair or likely biased
-End Expander:
----
-Image: pages/modules/Bonus/astro_thumbsup.jpg[1024]
-
-
-
-
-
-

pages/modules/Bonus/flashcards - Copy.html

@@ -1,182 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8">
-  <title>Statistical Inference Flashcards</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background: #f0f0f0;
-      margin: 0;
-      padding: 20px;
-    }
-    h1 {
-      text-align: center;
-    }
-    .flashcard {
-      position: relative;
-      width: 300px;
-      height: 200px;
-      margin: 40px auto;
-      padding: 20px;
-      background: #fff;
-      border-radius: 8px;
-      box-shadow: 0 4px 8px rgba(0,0,0,0.2);
-      display: flex;
-      align-items: center;
-      justify-content: center;
-      text-align: center;
-      font-size: 18px;
-      cursor: pointer;
-      user-select: none;
-    }
-    .checkmark {
-      position: absolute;
-      top: 10px;
-      right: 10px;
-      font-size: 24px;
-      color: gray;
-      cursor: pointer;
-    }
-    #controls {
-      text-align: center;
-      margin-top: 20px;
-    }
-    #controls button {
-      padding: 8px 16px;
-      margin: 0 10px;
-      font-size: 16px;
-    }
-    #controls button:disabled {
-      opacity: 0.5;
-      cursor: not-allowed;
-    }
-  </style>
-</head>
-<body>
-  <h1>Statistical Inference Flashcards</h1>
-
-  <div id="flashcard" class="flashcard">
-    <div id="checkmark" class="checkmark">✔</div>
-    <div id="content"></div>
-  </div>
-
-  <div id="controls">
-    <button id="prev">Previous</button>
-    <button id="next">Next</button>
-  </div>
-
-  <script>
-    // 10 sample flashcards
-    const flashcards = [
-      {
-        question: "What is a null hypothesis?",
-        answer: "A statement that there is no effect or difference, which we test against alternative evidence.",
-        correct: false
-      },
-      {
-        question: "What is a p-value?",
-        answer: "The probability of observing data at least as extreme as yours if the null hypothesis is true.",
-        correct: false
-      },
-      {
-        question: "What does a confidence interval tell you?",
-        answer: "A range of values where we believe the true parameter lies, with a certain level of confidence (e.g., 95%).",
-        correct: false
-      },
-      {
-        question: "What is a Type I error?",
-        answer: "Incorrectly rejecting the null hypothesis when it is actually true (a false positive).",
-        correct: false
-      },
-      {
-        question: "What is a Type II error?",
-        answer: "Failing to reject the null hypothesis when it is actually false (a false negative).",
-        correct: false
-      },
-      {
-        question: "What is statistical power?",
-        answer: "The probability of correctly rejecting a false null hypothesis (1 minus the Type II error rate).",
-        correct: false
-      },
-      {
-        question: "What is the difference between one-tailed and two-tailed tests?",
-        answer: "One-tailed tests check for an effect in a specific direction; two-tailed tests check for any effect in either direction.",
-        correct: false
-      },
-      {
-        question: "What is a t-test used for?",
-        answer: "Comparing the means of two groups to see if they are statistically different from each other.",
-        correct: false
-      },
-      {
-        question: "What is the bootstrap method?",
-        answer: "A technique that estimates the sampling distribution by repeatedly resampling the data with replacement.",
-        correct: false
-      },
-      {
-        question: "What is Bayesian inference?",
-        answer: "An approach that updates the probability of a hypothesis based on prior beliefs and new evidence.",
-        correct: false
-      }
-    ];
-
-    let currentIndex = 0;
-    let showAnswer = false;
-
-    const cardEl    = document.getElementById('flashcard');
-    const contentEl = document.getElementById('content');
-    const checkEl   = document.getElementById('checkmark');
-    const prevBtn   = document.getElementById('prev');
-    const nextBtn   = document.getElementById('next');
-
-    function renderCard() {
-      const card = flashcards[currentIndex];
-
-      if (showAnswer) {
-        contentEl.textContent = card.answer;
-        checkEl.style.display = 'none';
-      } else {
-        contentEl.textContent = card.question;
-        checkEl.style.display = 'block';
-        checkEl.style.color = card.correct ? 'green' : 'gray';
-      }
-
-      prevBtn.disabled = currentIndex === 0;
-      nextBtn.disabled = currentIndex === flashcards.length - 1;
-    }
-
-    // Flip card on click (except when clicking the checkmark)
-    cardEl.addEventListener('click', () => {
-      showAnswer = !showAnswer;
-      renderCard();
-    });
-
-    // Toggle correct mark
-    checkEl.addEventListener('click', e => {
-      e.stopPropagation(); // don't also flip the card
-      flashcards[currentIndex].correct = !flashcards[currentIndex].correct;
-      renderCard();
-    });
-
-    prevBtn.addEventListener('click', () => {
-      if (currentIndex > 0) {
-        currentIndex--;
-        showAnswer = false;
-        renderCard();
-      }
-    });
-
-    nextBtn.addEventListener('click', () => {
-      if (currentIndex < flashcards.length - 1) {
-        currentIndex++;
-        showAnswer = false;
-        renderCard();
-      }
-    });
-
-    // Initial display
-    renderCard();
-  </script>
-</body>
-</html>

pages/modules/Bonus/flashcards.html

@@ -1,180 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8">
-  <title>Statistical Inference Flashcards</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background: #f0f0f0;
-      margin: 0;
-      padding: 20px;
-    }
-    h1 {
-      text-align: center;
-    }
-    .flashcard {
-      position: relative;
-      width: 400px;
-      height: 400px;
-      margin: 40px auto;
-      padding: 20px;
-      background: #fff;
-      border-radius: 8px;
-      box-shadow: 0 4px 8px rgba(0,0,0,0.2);
-      display: flex;
-      align-items: center;
-      justify-content: center;
-      text-align: center;
-      font-size: 18px;
-      cursor: pointer;
-      user-select: none;
-    }
-    .checkmark {
-      position: absolute;
-      top: 10px;
-      right: 10px;
-      font-size: 24px;
-      color: gray;
-      cursor: pointer;
-    }
-    #controls {
-      text-align: center;
-      margin-top: 20px;
-    }
-    #controls button {
-      padding: 8px 16px;
-      margin: 0 10px;
-      font-size: 16px;
-    }
-    #controls button:disabled {
-      opacity: 0.5;
-      cursor: not-allowed;
-    }
-  </style>
-</head>
-<body>
-  <div id="flashcard" class="flashcard">
-    <div id="checkmark" class="checkmark">✔</div>
-    <div id="content"></div>
-  </div>
-
-  <div id="controls">
-    <button id="prev">Previous</button>
-    <button id="next">Next</button>
-  </div>
-
-  <script>
-    // 10 sample flashcards
-    const flashcards = [
-      {
-        question: "What is a null hypothesis?",
-        answer: "A statement that there is no effect or difference, which we test against alternative evidence.",
-        correct: false
-      },
-      {
-        question: "What is a p-value?",
-        answer: "The probability of observing data at least as extreme as yours if the null hypothesis is true.",
-        correct: false
-      },
-      {
-        question: "What does a confidence interval tell you?",
-        answer: "A range of values where we believe the true parameter lies, with a certain level of confidence (e.g., 95%).",
-        correct: false
-      },
-      {
-        question: "What is a Type I error?",
-        answer: "Incorrectly rejecting the null hypothesis when it is actually true (a false positive).",
-        correct: false
-      },
-      {
-        question: "What is a Type II error?",
-        answer: "Failing to reject the null hypothesis when it is actually false (a false negative).",
-        correct: false
-      },
-      {
-        question: "What is statistical power?",
-        answer: "The probability of correctly rejecting a false null hypothesis (1 minus the Type II error rate).",
-        correct: false
-      },
-      {
-        question: "What is the difference between one-tailed and two-tailed tests?",
-        answer: "One-tailed tests check for an effect in a specific direction; two-tailed tests check for any effect in either direction.",
-        correct: false
-      },
-      {
-        question: "What is a t-test used for?",
-        answer: "Comparing the means of two groups to see if they are statistically different from each other.",
-        correct: false
-      },
-      {
-        question: "What is the bootstrap method?",
-        answer: "A technique that estimates the sampling distribution by repeatedly resampling the data with replacement.",
-        correct: false
-      },
-      {
-        question: "What is Bayesian inference?",
-        answer: "An approach that updates the probability of a hypothesis based on prior beliefs and new evidence.",
-        correct: false
-      }
-    ];
-
-    let currentIndex = 0;
-    let showAnswer = false;
-
-    const cardEl    = document.getElementById('flashcard');
-    const contentEl = document.getElementById('content');
-    const checkEl   = document.getElementById('checkmark');
-    const prevBtn   = document.getElementById('prev');
-    const nextBtn   = document.getElementById('next');
-
-    function renderCard() {
-      const card = flashcards[currentIndex];
-
-      if (showAnswer) {
-        contentEl.textContent = card.answer;
-        checkEl.style.display = 'none';
-      } else {
-        contentEl.textContent = card.question;
-        checkEl.style.display = 'block';
-        checkEl.style.color = card.correct ? 'green' : 'gray';
-      }
-
-      prevBtn.disabled = currentIndex === 0;
-      nextBtn.disabled = currentIndex === flashcards.length - 1;
-    }
-
-    // Flip card on click (except when clicking the checkmark)
-    cardEl.addEventListener('click', () => {
-      showAnswer = !showAnswer;
-      renderCard();
-    });
-
-    // Toggle correct mark
-    checkEl.addEventListener('click', e => {
-      e.stopPropagation(); // don't also flip the card
-      flashcards[currentIndex].correct = !flashcards[currentIndex].correct;
-      renderCard();
-    });
-
-    prevBtn.addEventListener('click', () => {
-      if (currentIndex > 0) {
-        currentIndex--;
-        showAnswer = false;
-        renderCard();
-      }
-    });
-
-    nextBtn.addEventListener('click', () => {
-      if (currentIndex < flashcards.length - 1) {
-        currentIndex++;
-        showAnswer = false;
-        renderCard();
-      }
-    });
-
-    // Initial display
-    renderCard();
-  </script>
-</body>
-</html>

pages/modules/Bonus/navigational_map.html

@@ -1,176 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8">
-  <title>Navigational Map</title>
-  <style>
-    body { font-family: sans-serif; }
-    svg  { border: 1px solid #ccc; }
-    rect { cursor: pointer; }
-  </style>
-</head>
-<body>                               
-  <svg id="tree" width="1000" height="600">
-    <defs>
-      <marker id="arrow"
-              markerWidth="10" markerHeight="10"
-              refX="0" refY="3"
-              orient="auto">
-        <path d="M0,0 L0,6 L9,3 z" fill="#000"/>
-      </marker>
-    </defs>
-    <g id="lines"></g>
-    <g id="nodes"></g>
-  </svg>
-
-  <!-- Script is placed *after* the SVG so we can initialize immediately -->
-  <script>
-  (function(){
-    var svg       = document.getElementById('tree'),
-        linesG    = document.getElementById('lines'),
-        nodesG    = document.getElementById('nodes'),
-        totalLvls = 5,
-        maxNodes  = Math.pow(2, totalLvls) - 1,     // 31
-        rectSize  = 35,
-        marginX   = 50,
-        hSpacing  = 150,
-        // parseFloat in case someone ever writes "600px"
-        W         = parseFloat(svg.getAttribute('width')),
-        H         = parseFloat(svg.getAttribute('height')),
-        shown     = {};   // tracks which indices are already drawn
-
-    // 1) generate labels A..Z, then 1..5
-    
-    var labels = [];
-
-    // Add Labels (= MaxNodes = for 5 levels = 2^5-1 = 32-1 = 31)
-
-    labels[ 1]  = "Start  ➤"
-    labels[ 2]  = "Mean" 
-    labels[ 3]  = "Frequency"
-    labels[ 4]  = "1 Mean"
-    labels[ 5]  = "2 Means"
-    labels[ 6]  = "1 Variable"
-    labels[ 7]  = "2 Variables"
-    labels[ 8]  = "Population"
-    labels[ 9]  = "Sample"
-
-    labels[10]  = "Dependent"
-    labels[11]  = "Independent"
-    labels[12]  = "" 
-    labels[13]  = "Chi Frequency   🚀"
-    labels[14]  = "Chi Independence🚀"
-    labels[15]  = ""
-    labels[16]  = ""
-    labels[17]  = "Z Score        🚀"
-    labels[18]  = ""
-    labels[19]  = "T Score        🚀"
-
-    labels[20]  = ""
-    labels[21]  = "Dependent T    🚀"
-    labels[22]  = "" 
-    labels[23]  = "Independent T  🚀"
-    labels[24]  = ""
-    labels[25]  = ""
-    labels[26]  = ""
-    labels[27]  = ""
-    labels[28]  = ""
-    labels[29]  = ""
-
-    labels[30]  = ""
-    labels[31]  = ""
-
-    var hide = [];
-
-    for (i = 1; i <= maxNodes; i++) {
-      if (labels[i]=="") {
-        hide[i]=true;
-      }
-    }
-
-    // 2) precompute (x,y) for each node
-
-    var pos = [];
-
-    for (i = 1; i <= maxNodes; i++) {
-      var lvl       = Math.floor(Math.log2(i)),
-          lvlStart  = Math.pow(2, lvl),
-          idxInLvl  = i - lvlStart,
-          nodesInLvl= lvlStart,
-          vSpacing  = H / (nodesInLvl + 1),
-          x         = marginX + lvl * hSpacing,
-          y         = vSpacing * (idxInLvl + 1);
-      pos[i] = { x: x, y: y };
-    }
-
-    // 3) draw one node (square + text) and hook up its click
-
-    function drawNode(i) {
-
-      if (i < 1 || i > maxNodes || shown[i]) return;
-      shown[i] = true;
-      var p = pos[i];
-
-      var g = document.createElementNS('http://www.w3.org/2000/svg','g');
-      var r = document.createElementNS('http://www.w3.org/2000/svg','rect');
-          r.setAttribute('x', p.x - rectSize/2);
-          r.setAttribute('y', p.y - rectSize/2);
-          r.setAttribute('width',   rectSize*6);
-          r.setAttribute('height',  rectSize);
-          r.setAttribute('fill',    '#b3d9ff');
-          r.setAttribute('stroke',  '#000');
-          r.setAttribute('cursor',  'pointer');
-
-      g.appendChild(r);
-
-      var t = document.createElementNS('http://www.w3.org/2000/svg','text');
-          t.setAttribute('x',              p.x - 10);
-          t.setAttribute('y',              p.y + 2);
-          t.setAttribute('text-anchor',    'left');
-          t.setAttribute('alignment-baseline','middle');
-          t.textContent = labels[i];
-      g.appendChild(t);
-
-      g.addEventListener('click', function(evt){
-        evt.stopPropagation();
-        expand(i);
-      });
-
-      nodesG.appendChild(g);
-    }
-
-    // 4) when you click a node, draw lines & show its two children
-
-    function expand(i) {
-      
-      var p = pos[i];
-
-      [2*i, 2*i + 1].forEach(function(c){
-
-        if (c <= maxNodes && !shown[c] && !hide[c] ) {
-
-          var cp = pos[c],
-          l  = document.createElementNS('http://www.w3.org/2000/svg','line');
-
-          l.setAttribute('x1', p.x);
-          l.setAttribute('y1', p.y);
-          l.setAttribute('x2', cp.x);
-          l.setAttribute('y2', cp.y);
-          l.setAttribute('stroke',      '#000');
-          l.setAttribute('marker-end', 'url(#arrow)');
-
-          linesG.appendChild(l);
-          drawNode(c);
-
-        }
-      });
-    }
-
-    // 5) very first call draws the root (node #1)
-
-    drawNode(1);   
-
-  })();
-  </script>
-</body>
-</html>

pages/modules/Bonus/solar_movie_crop.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c73f3fe54283894b06d3898e372c132eee73aac3cfad955458785ee9c5917c2f
-size 1473330

pages/modules/Copy_Overview.bat

@@ -1,21 +0,0 @@
-copy overview.txt c:.\Module_01
-copy overview.txt c:.\Module_02
-copy overview.txt c:.\Module_03
-copy overview.txt c:.\Module_04
-copy overview.txt c:.\Module_05
-copy overview.txt c:.\Module_06
-copy overview.txt c:.\Module_07
-copy overview.txt c:.\Module_08
-copy overview.txt c:.\Module_09
-copy overview.txt c:.\Module_10
-copy overview.txt c:.\Module_11
-copy overview.txt c:.\Module_12
-copy overview.txt c:.\Module_13
-copy overview.txt c:.\Module_14
-copy overview.txt c:.\Module_15
-copy overview.txt c:.\Module_16
-copy overview.txt c:.\Module_17
-copy overview.txt c:.\Module_18
-copy overview.txt c:.\Module_19
-copy overview.txt c:.\Module_20
-copy overview.txt c:.\Overview

pages/modules/Module_01/Inference Error Types.txt

@@ -1,24 +0,0 @@
-Inference Error Types
-
-
-                     True                 False
-
-Predict Positive     True Positive        Type 1  
-                                      (False Positive)
-
-Predict Negative     Type 2            True Negative
-                 (False Negative)
-
-
-##### Type 1 (False Positive)
-Reject Null when true 
-
-Alpha is the probability of committing a Type 1 error  
-
-##### Type 2 (False Negative)
-Reject Null when you should not
-
-Beta is the probability of committing a Type 2 error 
-
-
----

pages/modules/Module_01/Overview.txt

@@ -1,175 +0,0 @@
-Introduction to Statistical Inference
----
-#### What is Statistical Inference
-
-Statistical inference is the process of using data from a limited set of observations to draw conclusions about a broader situation. 
-It is like observing a handful of events and then making an educated guess about how things work in general.
-
-We are going to use the example of rolling two dice and looking at their sum as a foundation for our sample questions.  
-We are trying to answer the question “Are these dice fair?”.  
-Let’s go through the process without diving into calculations yet.
-
----
-What is Hypothesis Testing?
-
-This is about make decisions (or inferences) about a population based on sample data
-
-We set up two competing hypotheses about a population:  
-
-1) Null hypothesis (baseline assumption - nothing else to be found)
-or any other observed effect from sample data is due to chance or random variation
-
-2) Alternative hypothesis (opposite from the baseline assumption)
-observed sample data differs in a statistically meaningful way from the baseline assumption
-
-Hypothesis Testing
-
-A statistical test to gather evidence to either:
-
-1) reject the null (baseline) hypothesis in favor of the alternative hypothesis or 
-
-2) fail to reject the null hypothesis - there's not enough evidence to support the alternative
----
-#### Statistical Inference Walkthrough
-
-Expander: Hypothesis Setup - Frame Your Question as Two Competing Stories
-Base story (“null hypothesis”): The dice are fair
-Alternative story: The dice are biased so low or high sums occur more often than with fair dice
-End Expander:
-Expander: Gather Your Evidence
-Decide how many times you will roll the dice.
-Keep track of the outcome (the sum) for each roll.
-End Expander:
-Expander: Summarize What You See
-Organize your recorded sums in a simple way (e.g count how often each sum appears)
-Look for patterns — do certain sums show up more or less often?
-End Expander:
-Expander: Compare to What You’d Expect If the Dice Were Fair
-Ask yourself: “Do the patterns I see match what typically happens with fair dice?”
-Remember even fair dice will show high or low counts in a limited set of rolls
-End Expander:
-Expander: Judge whether the Differences Are Random or Suggest Bias
-Small fluctuations are normal when you roll only a few times
-Very large or consistent departures from the fair-dice pattern might indicate bias
-End Expander:
-Expander: Reach a Conclusion
-If your observed pattern looks fair dice allowing for normal ups and downs, you observe the dice are fair
-If the pattern is so skewed it seems unlikely, you go with the belief the dice are biased
-End Expander:
-Expander: Summary of statistical inference
-• Start with a clear question (fair vs. biased dice).
-• Collect and summarize real-world data (rolls and sums).
-• Judge how well that data lines up with the “fair dice” story.
-• Make a reasoned decision whether the dice are fair or likely biased
-End Expander:
-
----
-Image: pages/modules/Module_01/checkpoint_banner.jpg
-
-Cognitive Checkpoint: [01-01-01]
-- Describe what statistical inference is and how it is applied 
-- Using the example of a coin toss walkthrough the steps for a statistical inference test
-- What is your base case (null hypothesis) and what is the alternative
-- You flip the coin 100 times to get your sample data.  
-- What would you expect to see when you compare different samples?
-- What would you expect to see with different sample sizes?
-End Cognitive Checkpoint:
-
----
-We provide a sample walkthrough for a coin toss problem below
-
-Expander: Frame Your Question as Two Competing Stories
-You would like to evaluate whether a coin is properly weighted (fair) and has an equal chance of landing heads or tails.
-• Base assumption: 
-Assuming the coin is fair. After several coin flips you’d expect about half heads and half tails.
-• Alternative case: 
-The coin might actually be unfair (biased) giving heads more or less often than tails.
-End Expander:
-Expander: Gather Your Evidence
-• Decide how many times you will flip the coin for fixed number of times (eg 100 flips) 
-• Count how many heads you get
-• Keep track of the outcome
-End Expander:
-Expander: Summarize What You See
-• Organize your recorded sums in a simple way
-• Look for patterns — do certain sums show up more or less often?
-End Expander:
-Expander: Compare to What You’d Expect If the Coin Was Fair
-• Calculate what the outcomes would be if the coin were really fair.
-• For example, if you get 60 heads out of 100, ask “How likely is it to see 60 or more heads just by chance with a fair coin?”
-• Remember even fair coin will show high or low counts in a limited set of rolls
-End Expander:
-Expander: Judge whether the Differences Are Random or Suggest Bias
-• Define the notion of Surprise 
-• Pick a cutoff (for example, a 5% chance) for what counts as “surprising.” This would be regarded as an extreme result. If the chance of getting a result as extreme as you did is past that "surprising" cutoff, then you doubt the coin-is-fair assumption.
-End Expander:
-Expander: Reach a Conclusion
-• If your observed pattern looks fair allowing for normal ups and downs, you observe the coin is fair
-• If the pattern is so skewed it seems unlikely, you go with the belief the coin is biased
-End Expander:
----
-In the last section we asked you to flip a coin 100 times as your sample data.  We provided a simulator that does that by rolling two dice (below).  
-You can select the number of times you wish to roll the dice for your sample size.  Note the difference in graphs as you roll more samples.  
-Note the difference when you increase the number of rolls. We will address why this occurs in subsequent sessions.
-HTML: dice_sim.html[1000]
----
-Image: pages/modules/Module_01/checkpoint_banner.jpg
-
-Cognitive Checkpoint: [01-01-02]
-- Describe what statistical inference is and how it is applied 
-- Using the example of "Do unicorns exist", walk through the steps for a statistical inference test
-- Hint: your base case (null hypothesis) is unicorns do not exist and you are trying to prove otherwise
-- Can a statistic test even if statistically significant prove a fact (e.g. what are its limitations) ?
-End Cognitive Checkpoint:
----
-#### Unicorn Example
-
-##### Step 1: State the Null Hypothesis 
-Default assumption: "Unicorns do not exist" (yes, I am disappointed too)
-We assume this is true unless we find strong enough evidence against it.
-
-##### Step 2: Collect Evidence
-We look for evidence to challenge this assumption. 
-For example, you might go on a quest, explore forests, check museums, or ask people if they've seen unicorns.
-
-##### Step 3: Evaluate the Evidence  
-Assess whether the evidence strongly supports the idea that unicorns actually exist. 
-
-##### Step 4: Make a Decision 
-
-If the evidence is not strong enough, you stick with the null hypothesis — “Unicorns do not exist.” 
-This doesn't prove unicorns absolutely don’t exist, it just means you haven't found good enough evidence to say they do.
-If the evidence is very strong — maybe someone finds a real unicorn horn, or a clear photo 
-— then you reject the null hypothesis and think instead that unicorns might exist.
-
-##### Summary
-
-Hypothesis testing is a process of starting with a default idea (null hypothesis), looking for evidence, 
-and then deciding whether the evidence is strong enough to reject that idea or not. 
-It’s like being a detective: you assume something is true until you find good reasons to think otherwise.
-
-In this case, you start by assuming unicorns don’t exist, and only change your mind if you find convincing proof that they do.
----
-Image: pages/modules/Module_01/astro_unicorn.jpg[512]
-
-#### Why can't we prove the null hypothesis (Unicorns don't exist)
-
-Proving a negative claim is inherently difficult because for any observation there are always multiple possible hypotheses that could explain the data, and it's impossible to test all of them. 
-
-Imagine trying to prove there are no unicorns in the world - you'd need to examine every possible location and check every corner of the globe. For hypothesis testing, proving the null hypothesis (no effect) requires examining all possible alternative explanations for the observed data, which is impossible.
-
-Also for any set of results, there are always multiple hypotheses that could be true. For example, if you observe a small difference between two groups, you might initially think there's no significant difference (null hypothesis). However, other factors like random chance, measurement error, or even a small, true effect might also explain the observed difference. 
-
-In scientific hypothesis testing, the goal isn't to prove the null hypothesis but to gather evidence against it. If the evidence is strong enough, you can reject the null and suggest an alternative explanation. 
-
-Statistical limitations: The null hypothesis is often based on statistical assumptions about populations, and we typically only have data from a sample. This means our conclusions are limited to the sample and may not generalize to the entire population.
----
-HTML: flashcards01.html[800]
----
-Image: pages/modules/Module_01/blastoff.jpg
-#### Congratulations on Completing Basic Training
-
-
-
-
-

pages/modules/Module_01/dice_sim.html

@@ -1,175 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8">
-  <title>Dice Roll Simulator with Mean Line</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      margin: 20px;
-    }
-    #controls {
-      margin-bottom: 20px;
-    }
-    label {
-      margin-right: 10px;
-    }
-    #histogram {
-      border: 1px solid #ccc;
-      background: #fafafa;
-    }
-  </style>
-</head>
-<body>
-  <div id="controls">
-    <label for="bias">Bias:</label>
-    <select id="bias">
-      <option value="fair">Fair</option>
-      <option value="low">Biased Low</option>
-      <option value="high">Biased High</option>
-    </select>
-
-    <label for="numRolls">Rolls (1–1000):</label>
-    <input type="number" id="numRolls" value="100" min="1" max="1000">
-
-    <button id="rollBtn">Roll</button>
-  </div>
-
-  <canvas id="histogram" width="700" height="400"></canvas>
-
-  <script>
-  (function() {
-    const canvas = document.getElementById('histogram');
-    const ctx = canvas.getContext('2d');
-    const rollBtn = document.getElementById('rollBtn');
-    const biasSelect = document.getElementById('bias');
-    const numRollsInput = document.getElementById('numRolls');
-
-    // Weight tables for each bias
-    const weights = {
-      fair: [0,1,1,1,1,1,1],
-      low:  [0,6,5,4,3,2,1],
-      high: [0,1,2,3,4,5,6]
-    };
-    // Build CDFs
-    const cdf = {};
-    for (let mode in weights) {
-      const w = weights[mode];
-      cdf[mode] = [0];
-      let tot = 0;
-      for (let i = 1; i <= 6; i++) {
-        tot += w[i];
-        cdf[mode][i] = tot;
-      }
-      cdf[mode].total = tot;
-    }
-
-    function sampleDie(mode) {
-      const r = Math.random() * cdf[mode].total;
-      for (let face=1; face<=6; face++) {
-        if (r < cdf[mode][face]) return face;
-      }
-      return 6;
-    }
-
-    function simulate(n, mode) {
-      const freq = [];
-      for (let s=2; s<=12; s++) freq[s]=0;
-      let sumAll = 0;
-      for (let i=0; i<n; i++) {
-        const d1 = sampleDie(mode),
-              d2 = sampleDie(mode),
-              s = d1 + d2;
-        freq[s]++;
-        sumAll += s;
-      }
-      const mean = sumAll / n;
-      return {freq, mean};
-    }
-
-    function drawHistogram(freq, n, mean) {
-      // clear
-      ctx.clearRect(0,0,canvas.width,canvas.height);
-
-      // margins
-      const m = {top:40, right:20, bottom:40, left:50};
-      const w = canvas.width - m.left - m.right;
-      const h = canvas.height - m.top - m.bottom;
-
-      // background panel
-      ctx.fillStyle = 'rgba(255,255,255,0.8)';
-      ctx.fillRect(m.left, m.top, w, h);
-
-      // find max freq
-      let maxF=1;
-      for (let s=2; s<=12; s++) if ((freq[s]||0) > maxF) maxF = freq[s];
-
-      // axes
-      ctx.strokeStyle = "#333";
-      ctx.lineWidth = 1;
-      ctx.beginPath();
-      ctx.moveTo(m.left, m.top);
-      ctx.lineTo(m.left, m.top+h);
-      ctx.lineTo(m.left+w, m.top+h);
-      ctx.stroke();
-
-      // bars
-      const bins = 11;
-      const barW = (w / bins) * 0.8;
-      const gap  = (w / bins) * 0.2;
-
-      ctx.textAlign = "center";
-      ctx.fillStyle = "#333";
-      ctx.font = "14px sans-serif";
-
-      for (let s=2; s<=12; s++) {
-        const f = freq[s]||0;
-        const x = m.left + (s-2)*(barW+gap) + gap/2;
-        const barH = (f/maxF) * (h - 10);
-        const y = m.top + h - barH;
-        // draw bar
-        ctx.fillStyle = "#4a90e2";
-        ctx.fillRect(x, y, barW, barH);
-        // count above
-        ctx.fillStyle = "#000";
-        ctx.fillText(f, x + barW/2, y - 5);
-        // sum label below
-        ctx.fillText(s, x + barW/2, m.top + h + 20);
-      }
-
-      // y-axis labels
-      ctx.textAlign = "right";
-      ctx.fillText("0", m.left - 5, m.top + h);
-      ctx.fillText(maxF, m.left - 5, m.top + 5);
-
-      // draw mean line
-      const xMean = m.left + ((mean - 2) / 10) * w;
-      ctx.strokeStyle = "red";
-      ctx.lineWidth = 2;
-      ctx.beginPath();
-      ctx.moveTo(xMean, m.top);
-      ctx.lineTo(xMean, m.top + h);
-      ctx.stroke();
-      // label mean
-      ctx.fillStyle = "red";
-      ctx.textAlign = "center";
-      ctx.fillText("Mean: " + mean.toFixed(2), xMean, m.top - 10);
-    }
-
-    rollBtn.addEventListener('click', () => {
-      let n = parseInt(numRollsInput.value,10);
-      if (isNaN(n) || n<1) n=1;
-      if (n>1000) n=1000;
-      numRollsInput.value = n;
-
-      const mode = biasSelect.value;
-      const {freq, mean} = simulate(n, mode);
-      drawHistogram(freq, n, mean);
-    });
-
-    // initial blank draw
-    drawHistogram({}, 0, 2);
-  })();
-  </script>
-</body>
-</html>

pages/modules/Module_01/flashcards01.html

@@ -1,165 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8">
-  <title>Statistical Inference Flashcards</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background: #f0f0f0;
-      margin: 0;
-      padding: 20px;
-    }
-    h1 {
-      text-align: center;
-    }
-    .flashcard {
-      position: relative;
-      width: 300px;
-      height: 200px;
-      margin: 40px auto;
-      padding: 20px;
-      background: #fff;
-      border-radius: 8px;
-      box-shadow: 0 4px 8px rgba(0,0,0,0.2);
-      display: flex;
-      align-items: center;
-      justify-content: center;
-      text-align: center;
-      font-size: 18px;
-      cursor: pointer;
-      user-select: none;
-    }
-    .checkmark {
-      position: absolute;
-      top: 10px;
-      right: 10px;
-      font-size: 24px;
-      color: gray;
-      cursor: pointer;
-    }
-    #controls {
-      text-align: center;
-      margin-top: 20px;
-    }
-    #controls button {
-      padding: 8px 16px;
-      margin: 0 10px;
-      font-size: 16px;
-    }
-    #controls button:disabled {
-      opacity: 0.5;
-      cursor: not-allowed;
-    }
-  </style>
-</head>
-<body>
-  <div id="flashcard" class="flashcard">
-    <div id="checkmark" class="checkmark">✔</div>
-    <div id="content"></div>
-  </div>
-
-  <div id="controls">
-    <button id="prev">Previous</button>
-    <button id="next">Next</button>
-  </div>
-
-  <script>
-    // 10 sample flashcards
-    const flashcards = [
-      {
-        question: "What is a null hypothesis or base case?",
-        answer: "A statement that there is no effect or difference, which we test against alternative evidence.",
-        correct: false
-      },
-      {
-        question: "What is a p-value?",
-        answer: "The probability of observing data at least as extreme as yours if the null hypothesis is true.",
-        correct: false
-      },
-      {
-        question: "What does a confidence interval tell you?",
-        answer: "A range of values where we believe the true parameter lies, with a certain level of confidence (e.g., 95%).",
-        correct: false
-      },
-      {
-        question: "What is a Type I error?",
-        answer: "Incorrectly rejecting the null hypothesis when it is actually true (a false positive).",
-        correct: false
-      },
-      {
-        question: "What is a Type II error?",
-        answer: "Failing to reject the null hypothesis when it is actually false (a false negative).",
-        correct: false
-      },
-      {
-        question: "What is the difference between one-tailed and two-tailed tests?",
-        answer: "One-tailed tests check for an effect in a specific direction; two-tailed tests check for any effect in either direction.",
-        correct: false
-      },
-      {
-        question: "What are the steps used to frame a statistical inference problem?",
-        answer: "1) Frame question as two competing theories 2) Gather evidence 3) Summarize what you see 4) Compare to Expectations 5) Is this statistically probable? 6) Are differences random or biased 7) Conclude (Fail to Reject or Reject)",
-        correct: false
-      }
-    ];
-
-    let currentIndex = 0;
-    let showAnswer = false;
-
-    const cardEl    = document.getElementById('flashcard');
-    const contentEl = document.getElementById('content');
-    const checkEl   = document.getElementById('checkmark');
-    const prevBtn   = document.getElementById('prev');
-    const nextBtn   = document.getElementById('next');
-
-    function renderCard() {
-      const card = flashcards[currentIndex];
-
-      if (showAnswer) {
-        contentEl.textContent = card.answer;
-        checkEl.style.display = 'none';
-      } else {
-        contentEl.textContent = card.question;
-        checkEl.style.display = 'block';
-        checkEl.style.color = card.correct ? 'green' : 'gray';
-      }
-
-      prevBtn.disabled = currentIndex === 0;
-      nextBtn.disabled = currentIndex === flashcards.length - 1;
-    }
-
-    // Flip card on click (except when clicking the checkmark)
-    cardEl.addEventListener('click', () => {
-      showAnswer = !showAnswer;
-      renderCard();
-    });
-
-    // Toggle correct mark
-    checkEl.addEventListener('click', e => {
-      e.stopPropagation(); // don't also flip the card
-      flashcards[currentIndex].correct = !flashcards[currentIndex].correct;
-      renderCard();
-    });
-
-    prevBtn.addEventListener('click', () => {
-      if (currentIndex > 0) {
-        currentIndex--;
-        showAnswer = false;
-        renderCard();
-      }
-    });
-
-    nextBtn.addEventListener('click', () => {
-      if (currentIndex < flashcards.length - 1) {
-        currentIndex++;
-        showAnswer = false;
-        renderCard();
-      }
-    });
-
-    // Initial display
-    renderCard();
-  </script>
-</body>
-</html>

pages/modules/Module_02/Overview.txt

@@ -1,122 +0,0 @@
-Next Steps - Adding Calculations
----
-In our introductory module, we discussed the concept of statistical inference We will expand on the topic in this section 
-adding more terminology and also the mathematic calculations that are required to validate a problem statistically.
-Image: pages/modules/Module_02/astro_float.jpg[1024]
----
-Image: pages/modules/Module_02/ideal_prob_fair_coin.jpg[947]
-Let’s get back to our coin toss example.  The graph above represents an ideal sample that one would expect if you tossed a fair coin 20 times.  
-Given that the probability of heads (or tails) is 50% if the coin is fair, one would expect 50% * 20 = 10 as the most likely count after 20 coin tosses with higher or lower counts falling from that level on both sides of the curve in a symmetrical pattern as shown by the curve.
----
-Image: pages/modules/Module_02/ideal_prob_fair_coin_ge_13.jpg[925]
-So what are the chances of getting 13 or greater heads after tossing a fair coin 20 times?  This area is shown with the green bars.  
-To calculate we add up each bar to get a total.  Because we are only dealing with one-tail of the curve, this is a “one-tail” calculation, 
-the right tail that denotes whether we are greater or equal to a certain value (13 heads).
----
-We can calculate this in R using the binomial function and adding (among many other ways) the probability for each number from 13 (hurdle rate) to 20 (max throws).
-= dbinom(Number of Heads, Total Throws, Probability)
-
-Press the green bar to run the program and after a few seconds should see the following answer that the probability is 
-approximately 13.2% written as "[1] 0.131588"
-
-R Code: 
-dbinom(13,20,0.5)+ dbinom(14,20,0.5)+ dbinom(15,20,0.5)+ dbinom(16,20,0.5)+ dbinom(17,20,0.5)+ dbinom(18,20,0.5)+ dbinom(19,20,0.5)+ dbinom(20,20,0.5)
-End R Code:
-
----
-
-Image: pages/modules/Module_02/ideal_prob_fair_coin_ge_13.jpg[925]
-
-So what are the chances of getting 7 or FEWER heads after tossing a fair coin 20 times?  This area is shown with the green bars.  
-To calculate we add up each bar to get a total.  Because we are only dealing with one-tail of the curve, this is a “one-tail” calculation, 
-the right tail that denotes whether we are greater or equal to a certain value (13 heads).
----
-We can calculate this in R using the binomial function and adding (among many other ways) the probability for each number from 0 (minimum) to 7 (hurdle rate).
-= dbinom(Number of Heads, Total Throws, Probability)
-
-Press the green bar to run the program and after a few seconds should see the following answer that the probability is 
-approximately 13.2% written as "[1] 0.131588"
-
-This is identical to 13 or greater showing how the curve is symmetrical around the mean of 10
-
-R Code: 
-dbinom(0,20,0.5)+dbinom(1,20,0.5)+ dbinom(2,20,0.5)+ dbinom(3,20,0.5)+ dbinom(4,20,0.5)+ dbinom(5,20,0.5)+ dbinom(6,20,0.5)+ dbinom(7,20,0.5)
-End R Code:
-
-
----
-We can calculate this in R using the binomial function and adding (among many other ways) the probability for each number from 13 (hurdle rate) to 20 (max throws).
-= dbinom(Number of Heads, Total Throws, Probability)
-
-Press the green bar to run the program and after a few seconds should see the following answer:
-
-[1] 0.131588
-
-that the probability is approximately 13.2%
-
-
-R Code: 
-dbinom(13,20,0.5)+ dbinom(14,20,0.5)+ dbinom(15,20,0.5)+ dbinom(16,20,0.5)+ dbinom(17,20,0.5)+ dbinom(18,20,0.5)+ dbinom(19,20,0.5)+ dbinom(20,20,0.5)
-End R Code:
-
----
-Image: pages/modules/Module_03/under_construction.jpg[1024]
-Exciting and engaging ASCEND content is coming soon
----
-#### Correctly use and interpret p-values in hypothesis testing.
-
-Below is a step-by-step recipe for using and interpreting p-values, followed by a simple “coin-flip” example.
-
-Formulate hypotheses
-• H₀ (null): The effect or difference you’re testing is zero (e.g. “coin is fair,” p = 0.5).
-• H₁ (alternative): The effect goes in a specific direction (one-tailed) or differs in either direction (two-tailed).
-
-Choose a significance level α
-• Common choices: 0.05, 0.01.
-• This is your acceptable probability of a Type I error (rejecting H₀ when it’s true).
-
-Collect data & compute a test statistic
-• E.g. for coin‐flips, count the number of heads X in n flips.
-• Under H₀, X∼Binomial(n, 0.5).
-
-Compute the p-value
-• Definition: p-value = P(observing a result at least as extreme as yours, assuming H₀ is true).
-• For a two-tailed coin-fairness test with n=100 and observed X=60:
-– P(X≥60 or X≤40) = 2·P(X≥60) ≃ 2·0.028 = 0.056.
-• For a one-tailed test (H₁: coin is biased toward heads), p-value = P(X≥60) ≃ 0.028.
-
-Compare p-value to α and decide
-• If p ≤ α → reject H₀ (“statistically significant”).
-• If p > α → fail to reject H₀ (“not statistically significant”).
-
-Interpret correctly
-• A p-value of 0.028 means: “If the coin really were fair, there’s a 2.8% chance of getting 60 or more heads in 100 flips.”
-• It is NOT the probability that H₀ is true.
-• “Failing to reject H₀” does not prove H₀; it just means the data aren’t strong enough to rule it out at your chosen α.
-
-–––
-#### Coin-flip example in full:
-
-H₀: coin is fair (p=0.5)
-H₁ (two-tailed): p≠0.5
-
-α = 0.05
-
-Data: n=100 flips, observed X=60 heads.
-
-Under H₀, X∼Binomial(100,0.5)
-
-P(X≥60) = sum_{k=60}^{100} (100 choose k)·0.5¹⁰⁰ ≃ 0.028
-Two-tailed p-value = 2·0.028 = 0.056
-
-Decision: 0.056 > 0.05 → fail to reject H₀ at α=0.05.
-
-Conclusion: The data do not provide strong enough evidence (at the 5% level) to conclude the coin is unfair. 
-However, since p is close to 0.05, you might collect more data before making a final judgment.
-
-–––
-#### Key reminders:
-• p-value quantifies how surprising your data are under H₀, not the probability H₀ is true.
-• Always state your α level in advance.
-• Avoid “p-hacking” (trying multiple tests until you get p<α).
-• Report exact p-values (e.g. p=0.028), not just “p<0.05.”