Regression and Linear Modeling: Best Practices and Modern Methods

1: A Nerdly Manifesto
2: Basic Estimation and Assumptions
3: Simple Linear Models With Continuous Dependent Variables: Simple Regression Analyses
4: Simple Linear Models With Continuous Dependent Variables: Simple ANOVA Analyses
5: Simple Linear Models With Categorical Dependent Variables: Binary Logistic Regression
6: Simple Linear Models With Polytomous Categorical Dependent Variables: Multinomial and Ordinal Logistic Regression
7: Simple Curvilinear Models
8: Multiple Independent Variables
9: Interactions Between Independent Variables: Simple moderation
10: Curvilinear Interactions Between Independent Variables
11: Poisson Models: Low-Frequency Count Data as Dependent Variables
12: Log-Linear Models: General Linear Models When All of Your Variables Are Unordered Categorical
13: A Brief Introduction to Hierarchical Linear Modeling
14: Missing Data in Linear Modeling
15: Trustworthy Science: Improving Statistical Reporting
16: Reliable Measurement Matters
17: Prediction in the Generalized Linear Model
18: Modeling in Large, Complex Samples: The Importance of Using Appropriate