Understanding Statistical Analysis and Modeling is a text for graduate and advanced undergraduate students in the social, behavioral, or managerial sciences seeking to understand the logic of statistical analysis. Robert Bruhl covers all the basic methods of descriptive and inferential statistics in an accessible manner by way of asking and answering research questions. Concepts are discussed in the context of a specific research project and the book includes probability theory as the basis for understanding statistical inference. Instructions on using SPSS® are included so that readers focus on interpreting statistical analysis rather than calculations. Tables are used, rather than formulas, to describe the various calculations involved with statistical analysis and the exercises in the book are intended to encourage students to formulate and execute their own empirical investigations.

# Chapter 14: Correlation Analysis and Linear Regression : Assessing the Covariability of Two Quantitative Properties

### 14.0 Learning Objectives

In this chapter, we discuss two related techniques for assessing a possible association between two quantitatively assessed properties of a set of phenomena. These related techniques—correlation analysis and regression analysis—are based on the measure of association said to be the covariance as developed from probability theory (Chapter 8). In this chapter, we discuss the following:

• constructing what is said to be a scatter plot to provide a preliminary assessment of a possible association relationship between two properties based on curve fitting;
• assessing the covariance of the occurrences of the two properties in terms of the correlation coefficient and then using the Central Limit Theorem to assess the statistical significance of that ...