Section 10.1 Introduction to Regression
In a previous chapter, we discussed correlation analysis, which helps us understand the degree of association between two or more variables. Regression analysis is closely linked to correlation analysis, but it offers a more sophisticated way to explore the relationships among variables. Regression is a broad term encompassing a set of statistical methods used for modeling the relationship between a dependent variable and one or more independent variables, such as simple linear regression, multiple linear regression, polynomial regression, logistic regression, and so on. According to Vogt and Johnson (2011), regression analysis serves three primary purposes:
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Predicting the change in a dependent variable for each one-unit increase in an independent variable.
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Predicting the change in a dependent variable associated with a one-unit change in a specific independent variable, while controlling for other independent variables.
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Assessing how much better we can explain or predict a dependent variable by considering all the independent variables together.
Regression analysis is a powerful tool for understanding and quantifying the relationships between variables and making predictions based on those relationships. In this chapter, we will review two forms of linear regression: simple linear regression and multiple linear regression.
