Linear Regression and Correlation

Summary :

This chapter uses the example of whether an auto mechanic's salary relates to his years of experience to introduce linear regression and correlation. It covers the basic ideas of linear regression and correlation, creating and interpreting a line of best fit, calculating and interpreting the correlation coefficient, and identifying outliers.

Do two variables move together?

The chapter opens with the question of whether two numeric variables are related, using the pairing of exam grades on two tests and of an auto mechanic's pay against years of experience as running examples, and discusses the basic ideas of linear regression and correlation as tools for answering it.

The line of best fit and the correlation coefficient

Students learn to create and interpret a line of best fit through a scatter of paired data points, and to calculate and interpret the correlation coefficient, a single number summarizing how strong and in what direction the linear relationship between the two variables runs.

Identifying outliers

The chapter also covers calculating and interpreting outliers, points that fall unusually far from the line of best fit, teaching students to recognize when a data point may be distorting the regression results and should be examined rather than automatically included in the analysis.