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Section 1: Advanced Statistics and Social Science
- Section 1.1: Statistics and Social Science
- Section 1.2: What are Linear Models
- Section 1.3: What is Regression Analysis?
- Section 1.4: Examining Data
- Section 1.5: Transforming Data
Section 2: Linear Models and Least Squares
- Section 2.1: Simple Regression
- Section 2.2: Multiple Regression
- Section 2.3: Control
- Section 2.4: Regression Coefficients
- Section 2.5: Statistical Inference and Regression
Section 3: Regression Core Concepts
- Section 3.1: Variance Partitioning
- Section 3.2: Analysis of Effects
- Section 3.3: Categorical Independent Variables and Factorial Designs
- Section 3.4: Curvilinear Regression Analysis
- Section 3.5: Dummy Variables
Section 4: Analysis of Variance
- Section 4.1: One-Way Analysis of Variance
- Section 4.2: Two-Way Analysis of Variance
- Section 4.3: Analysis of Covariance
- Section 4.4: Examining Interactions
- Section 4.5: Repeated Measures Designs
Section 5: Linear Model Diagnostics
- Section 5.1: Dealing with Outliers
- Section 5.2: Examining Nonlinearity
- Section 5.3: Error Variance
- Section 5.4: Nonnormality
- Section 5.5: Collinearity
Section 6: Logit and Probit Models
- Section 6.1: The Purpose of Logistic Regression
- Section 6.2: Models for Dichotomous Data
- Section 6.3: The Linear Probability Model
- Section 6.4: Models for Polytomous Data
- Section 6.5: Assumptions and Limitations
Section 7: Advanced Regression Models
- Section 7.1: Causal Modeling
- Section 7.2: Path Analysis
- Section 7.3: Assumptions
- Section 7.4: Structural Equation Models with Latent Variables
- Section 7.5: Does the Model Fit?
Section 8: Other Advanced Linear Models
- Section 8.1: Canonical Correlation
- Section 8.2: Multivariate Analysis of Variance
- Section 8.3: Discriminate Analysis
- Section 8.4: Factor Analysis
- Section 8.5: Some Final Thoughts
File Created: 08/09/2018 Last Modified: 08/27/2019
This work is licensed under an Open Educational Resource-Quality Master Source (OER-QMS) License.
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