Regression analysis is arguably the single most powerful and widely applicable tool in any effective examination of common business issues. Every day, decision-makers face problems that require constructive actions with significant consequences, and regression procedures can prove a meaningful and valuable asset in the decision-making process. This text is designed to help students achieve a full understanding of regression and the many ways it can be used.
Taking into consideration current statistical technology, Introductory Regression Analysis focuses on the use and interpretation of software, while also demonstrating the logic, reasoning, and calculations that lie behind any statistical analysis. Furthermore, the text emphasizes the application of regression tools to real-life business concerns. This multilayered, yet pragmatic approach fully equips students to derive the benefit and meaning of a regression analysis.
This text is designed to serve in a second undergraduate course in statistics, focusing on regression and its component features. The material presented in this text will build from a foundation of the principles of data analysis. Although previous exposure to statistical concepts would prove helpful, all the material needed for an examination of regression analysis is presented here in a clear and complete form.
Allen Webster is a Professor at Bradley University. He gained his Ph.D. in Economics from Florida State University, and both an M.S. and B.S. in Economics from Fort Hays State University.
1. Review of Basic Concepts 2. An Introduction to Regression and Correlation Analysis 3. Statistical Inferences in the Simple Regression Model 4. Multiple Regression: Using Two or More Predictor Variables 5. Residual Analysis and Model Specification 6. Using Qualitative and Limited Dependent Variables 7. Heteroscedasticity 8. Autocorrelation 9. Non-Linear Regression and the Selection of the Proper Functional Form 10. Simultaneous Equations: Two Stage Least Squares 11. Forecasting with Time Series Data and Distributed Lag Models