|
|
Product Description
Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.
Customers Who Bought This Item Also Bought
- Applied Regression: An Introduction (Quantitative Applications in the Social Sciences)
- A Stata Companion to Political Analysis
- On Course: A Week-by-Week Guide to Your First Semester of College Teaching
- Public Policy Analysis
- Applied Statistics Using Stata: A Guide for the Social Sciences
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd Edition
- Logistic Regression: A Primer (Quantitative Applications in the Social Sciences)
- Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences)
- Applied Regression: An Introduction (Quantitative Applications in the Social Sciences)
- Multiple Regression in Practice (Quantitative Applications in the Social Sciences)
*If this is not the "Understanding Regression Assumptions (Quantitative Applications in the Social Sciences)" product you were looking for, you can check the other results by clicking this link
.
Details were last updated on Feb 22, 2025 18:42 +08.
