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.

# Bivariate Linear Regression and Correlation and Linear Partial Regression and Correlation

### Learning Objectives

What you are expected not only to learn but to master in this chapter:

• The basic ideas underlying linear correlation and regression analysis
• What a scattergram is and how to construct one
• What a best fitting regression line and a best fitting linear equation are
• What the constants a and b in a linear regression equation represent and how to calculate them
• What positive, negative, and no relationships between two variables mean in regression and correlation analysis
• The difference between linear and nonlinear relationships
• What standardized betas (βs) are and why they are useful in statistical analysis
• What can go wrong when using linear regression equations for predictions
• The similarities and differences between linear regression and correlation analyses
• What a correlation ...
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