In this activity, students will determine, by examining a graph, if a data set has a positive or negative correlation coefficient. Then, they will find the linear regression equation and calculate the correlation coefficient. They will use this line to predict the value of y for a given x and vice-versa. Finally, students will investigate the effect of outliers and influential points on the regression equation and correlation coefficient.

In this activity, students will identify outliers that are influential with respect to the least-squares regression line. Students will describe the role of the location of a point relative to the other data in determining whether that point has influence on the least-squares regression line.

This lesson involves a least-squares regression line fit to a set of nine values.

This lesson involves predicting values of a particular variable.

This lesson involves investigating the connection between the scatterplot of bivariate data and the numerical value of the correlation coefficient.

This lesson involves analyzing the association between the number of spaces from Go and the cost of the property on a standard Monopoly board.

This lesson involves creating a scatterplot and fitting a line to student pulse rates collected before and after exercise.

Students investigate the method of least squares by adding the squares to a scatter plot and moving a line to find the minimum sum. Then they compare their line to the built-in linear regression model.

Students will collect data, find the linear regression model of the data, and address aspects of the data that affect regression.

This lesson involves square root, semi-log, and log-log transformations of curved bivariate data using given data sets.