Regression and other stories /
Many textbooks on regression focus on theory and the simplest examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is a book about how to use regression to solve real problems of comparison, estimation, prediction and causal infe...
| Main Authors: | , , |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge ; New York :
Cambridge University Press,
[2021]
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| Series: | Analytical methods for social research.
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| Subjects: |
Table of Contents:
- Preface
- Part 1: Fundamentals. Overview
- Data and measurement
- Some basic methods in mathematics and probability
- Statistical inference
- Simulation
- Part 2: Linear regression
- Background on regression modeling
- Linear regression with a single predictor
- Fitting regression models
- Prediction and Bayesian inference
- Linear regression with multiple predictors
- Assumptions, diagnostics, and model evaluation
- Transformations and regression
- Part 3: Generalized linear models. Logistic regression
- Working with logistic regression
- Other generalized linear models
- Part 4: Before and after fitting a regression. Design and sample size decisions
- Poststratification and missing-data imputation
- Part 5: Causal inference
- Causal inference and randomized experiments
- Causal inference using regression on the treatment variable
- Observational studies with all confounders assumed to be measured
- Additional topics in causal inference
- Part 6: What comes next? Advanced regression and multilevel models
- Appendixes. Computing in R
- 10 quick tips to improve your regression modeling.