1. Section 4.2 Regression Equations and Predictions

2. Regression Lines A regression line in an equation that is used to predict values for a response variable with linear association. Predicted Value of the response variable. y-intercept, (initial value) Slope, Rate of Change Explanatory Variable

3. Example: According to the data on crime rates in Florida contained on the text CD the regression equation below will predict the values for the number of crimes per 1000 people in terms of the percentage of people living in urban areas by county.

4. Example: According to the data on crime rates in Florida contained on the text CD the regression equation below will predict the values for the number of crimes per 1000 people in terms of the percentage of people living in urban areas by county. The y-intercept is the value of the response variable when the explanatory variable is zero. The Slope is the amount of change in the response variable for each change in the explanatory variable.

5. Residual: The residual is the difference between a predicted value and the true value for the response variable. Residual =

6. Finding Regression Lines and Correlation with Excel: Open up the Regression file on the Q.