Multiple Regression: A Practical Introduction is a text for an advanced undergraduate or beginning graduate course in statistics for social science and related fields. Drawing on decades of teaching this material, the authors present the ideas in an approachable and nontechnical manner, with no expectation that readers have more than a standard introductory statistics course as background. The book includes many interesting example analyses and interpretations, along with exercises.
Multiple Regression: A Practical Introduction is a text for an advanced undergraduate or beginning graduate course in statistics for social science and related fields. Drawing on decades of teaching this material, the authors present the ideas in an approachable and nontechnical manner, with no expectation that readers have more than a standard introductory statistics course as background. The book includes many interesting example analyses and interpretations, along with exercises.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Aki Roberts is Associate Professor in the Sociology Department at the University of Wisconsin-Milwaukee. Her research focuses on policing, crime statistics, and comparative criminology. She regularly teaches the two undergraduate statistics courses in her department, Soc. 261 (Introduction to Statistical Thinking in Sociology) and Soc. 461 (Multivariate Data Analysis for Social Research), as well as course for MA and and PhD students, Soc, . 761 (Advanced Statistical Methods in Sociology). The manuscript for this book has been class-tested in Soc. 461. John Roberts is Professor and Director of Graduate Studies in the Sociology Department at the University of Wisconsin-Milwaukee. His teaching and research interests are social networks, data analysis, and research methods.
Inhaltsangabe
Chapter 1 Introduction Chapter 2 Fundamentals of Multiple Regression Chapter 3 Categorical Independent Variables in Multiple Regression: Dummy Variables Chapter 4 Multiple Regression with Interaction Chapter 5 Logged Variables in Multiple Regression Chapter 6 Nonlinear Relationships in Multiple Regression Chapter 7 Categorical Dependent Variables: Logistic Regression Chapter 8 Count Dependent Variables: Poisson Regression Chapter 9 A Brief Tour of Some Related Methods