Class Overview

Below are materials from several statistics instructional tutorials. The class is conducted in R. Knowing R is not a prerequisite for the course, but having a basic understanding will be helpful.

The topics and links with instructional information and videos are listed on these pages. More content and instructional videos will be added frequently!

Class Github

Code and data associated with each course can be found on Github here: https://github.com/kelsey-huntzberry/DataAnalysisLab

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Class 1: Descriptive Statistics & Probability

• Measures of Central Tendency
• Measures of Spread
• Basic Terms
• Basic Probability Rules

Class 2: Statistical Inference

• Random Sampling & the Central Limit Theorem
• The Normal Distribution
• Standard Error
• Z-Scores

Class 3: Hypothesis Testing

• Confidence intervals
• Null and Alternative Hypotheses
• P-Values

Class 4: The T-Test

• Error Types & P-Value Cut Offs
• The T-Test
• T-Test with Pairs & Non-Normal Data

Class 5: Chi Square

• Introduction to Chi Square
• Chi Square Example Walk Through

Class 6: ANOVA

• One-Way ANOVA Overview
• One-Way ANOVA Example
• ANOVA Assumptions
• Two-Way ANOVA

Class 7: Correlation & Introduction to Linear Regression

• Correlation Overview
• Correlation Assumptions
• Correlation Significance Testing
• Linear Regression Overview
• Linear Regression Assumptions

Class 8: Regression Evaluation & Multiple Linear Regression

• Linear Regression Evaluation
• Multiple Regression and Multicollinearity
• Comparing Regression Models
• Regression Interaction Effects