## Category: Data Analysis

### Data Analysis Basics

- Identify Variable Types in Statistics
- Why Divide Sample Standard Deviation by n-1?
- An Example of Using Marginal and Conditional Distributions
- Correlation Coefficient vs Regression Coefficient
- When Does Correlation Imply Causation?
- P-Value: A Simple Explanation for Non-Statisticians
- 7 Tricks to Get Statistically Significant p-Values
- Statistical Power: What It Is and How It Is Used in Practice
- How to Handle Missing Data in Practice: Guide for Beginners

### Considerations Before Building a Regression Model

- When to Use Regression Analysis (With Examples)
- Which Variables Should You Include in a Regression Model?
- Using the 4 D-Separation Rules to Study a Causal Association
- 5 Variable Transformations to Improve Your Regression Model
- Square Root Transformation: A Beginner’s Guide
- Why Add & How to Interpret a Quadratic Term in Regression
- Correlation vs Collinearity vs Multicollinearity
- What is an Acceptable Value for VIF? (With References)
- Standardized vs Unstandardized Regression Coefficients
- Why and When to Include Interactions?

### Types of Regression Models

- Forward and Backward Stepwise Regression
- Regression with Best Subset Selection
- Regularized Regression
- Weighted Regression
- Regression Tree vs Linear Regression

### Interpreting the Output of a Regression Model

- Interpret the Linear Regression Intercept
- Interpret Linear Regression Coefficients
- Interpret Interactions in Linear Regression
- Interpret Log Transformations in Linear Regression
- Interpret Logistic Regression Intercept
- Interpret Logistic Regression Coefficients
- Interpret Poisson Regression Coefficients
- What is a Good R-Squared Value? [Based on Real-World Data]
- Understand the F-Statistic in Linear Regression
- Residual Standard Deviation/Error
- Relationship Between r and R-squared in Linear Regression
- Coefficient of Alienation, Non-Determination and Tolerance
- Deviance in the Context of Logistic Regression
- Assess Variable Importance in Linear and Logistic Regression