Methods for Statistics and Data Science
Chapter Zero
0.1
What Is A Statistical Model? (Chapter Zero)
0.1.1
Choose the Model
0.1.2
Fit the Model
0.1.3
Assess the Model
0.1.4
Use the Model
0.2
Introduction to R and R Studio
0.3
The Tidyverse
0.4
Data Wrangling & Visualization
0.5
Data Importing
0.6
Working with Probability Distributions
0.7
Webscraping
1
Simple Linear Regression
1.1
Correlation
1.2
Linear Model
1.3
Assumption of the Linear Model
1.4
Tidying Our Linear Model
1.5
Log Transfromation
1.6
Simple Linear Regression with the Planets
2
Inference for Simple Linear Regression
2.1
The ANOVA Table for Simple Regression
2.2
Inference For The Slope
2.3
Confidence and Prediction Bands
2.4
R Code, Galton’s Height Data
2.5
Confidence and Prediction Bands, Books Example
2.6
Confidence and Prediction Bands, Geysers
3
Multiple Linear Regression
3.1
The Model
3.2
The AccordPrices Example
3.3
The airquality Example
3.4
Muliple Regression Extras
3.5
Regression Diagnostics
3.6
Standardized Beta Coefficients
3.7
Effect Sizes
3.8
Home Prices data
4
Additional Topics In Regression
4.1
Three Concepts for Computationally Intensive Inference
4.2
Bootstrapping a CI for a Proportion
4.3
The Bootstrapping Principle
4.4
Bootstrapping a CI for a Mean
4.5
Bootstrapping a CI for the Slope of a Regression Model
4.6
Bootstrapping a CI for Interquartile Range
4.7
Permutation Test
4.8
Randomization Test
4.9
Bootstrapping and Randomization Testing for Correlation/Regression
5
One-Way ANOVA
5.1
Introduction/Assumptions
5.2
Cooking Pot Example
5.3
One-Way ANOVA Model
5.4
Sums of Squares
5.5
The ANOVA Table
5.6
One-WAY ANOVA with R
6
Two-Way ANOVA
6.1
Another Agriculture Example
6.2
The Randomized Block Design model
6.3
Sums of Squares
6.4
Using R
7
ANOVA with Interaction
7.1
The Interactive Two-Way ANOVA Model
7.2
Sums of Squares
7.3
ANOVA Table
7.4
Interactions (What Are They?)
8
Additional Topics In ANOVA
9
Logistic Regression
10
Multiple Logistic Regression
11
Categorical Data Analysis
12
Classical Nonparametric Statistics
13
Ethics in Statistics and Data Science
13.1
Milgram Experiment
13.2
Duke
13.3
Cambridge Analytica
13.4
\(P\)
-Hacking
13.5
Fisher controversy
Published with bookdown
STA 265 Notes (Methods of Statistics and Data Science)
Chapter 9
Logistic Regression