Summary
Contents
Subject index
Statistics in Criminal Justice and Criminology Research: An Introduction is for advanced undergraduate and graduate level students in criminology and criminal justice statistics courses. It is designed for students pursuing careers in criminology and/or criminal justice by adequately and evenly covering statistical research for both professions. The engaging writing style, real-life applications, and comprehensive format will distinguish this text from its competitors and help establish it as more than just another statistics book. Fitzgerald and Fitzgerald have teamed up to create a flexible and useful text that will not only meet the needs of criminal justice/criminology students but also provide motivation for students who have math anxiety yet strive to become criminal justice professionals. Features and Benefits: 1) Frequent use of diagrams and graphs to illustrate ideas and procedures discussed in the text. 2) Attention devoted to discussing “conceptual” formulas and what they represent about the data to help students make sense of the results. 3) Extensive sets of review questions and exercises at ends of chapters help students master the content presented. 4) Quotes from actual reports in “From the Literature” boxes help connect the discussion of research methods and statistical analysis with the research process as a whole. 5) “Pause, Think and Explore” boxes follow the mathematical formulas and are intended to help students develop an understanding of how the formula works, gain confidence in working with the mathematics, and develop better insight about what the formulas are signaling about the data being analyzed.
Multivariate Linear Regression and Correlation Analysis and Logistic Regression: An Introduction
Multivariate Linear Regression and Correlation Analysis and Logistic Regression: An Introduction
Learning Objectives
What you are expected not only to learn but to master in this chapter:
- The similarities and differences between correlation and regression analysis
- Some ways of dealing with missing data
- The assumptions of linear multiple regression and correlation analysis
- The basic ideas underlying multiple regression analysis
- How to graph a trivariate best fitting plane
- The components of a general multiple regression equation
- The nature and uses of standardized betas
- Some versions of stepwise regression
- The basic ideas underlying linear multiple correlation
- What dummy variables are and how they are used in multiple regression and correlation analyses
- What statistical models are and how they are used in multiple regression and correlation analyses
- What main effects and interaction effects are in multiple regression and correlation ...
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