shown below are three scatter plots and their lines of best fit. also shown are three residual plots. answer…

Explanation:

Step1: Recall residual plot concept

Residuals are the differences between the observed and predicted values. A good - fit line has randomly scattered residuals around the horizontal axis in the residual plot.

Step2: Analyze Residual Plot 1

Residual Plot 1 has residuals randomly scattered around the horizontal axis. This indicates a good - fit line. Scatter Plot A has a linear relationship and its residuals would likely be randomly scattered, so Residual Plot 1 matches Scatter Plot A. The line of best fit for Scatter Plot A is a good fit as the residuals are randomly distributed.

Step3: Analyze Residual Plot 2

Residual Plot 2 has all residuals close to zero and evenly distributed along the horizontal axis. This implies a perfect - fit (or near - perfect) horizontal line. Scatter Plot B has a horizontal relationship, so Residual Plot 2 matches Scatter Plot B. The line of best fit for Scatter Plot B is a horizontal line which fits the data well as there is no vertical variation in the data points with respect to the independent variable.

Step4: Analyze Residual Plot 3

Residual Plot 3 has a non - random pattern of residuals (a curve - like pattern). This indicates that the linear model is not a good fit for the data. Scatter Plot C has a non - linear (curved) relationship, so Residual Plot 3 matches Scatter Plot C. The line of best fit for Scatter Plot C is a poor fit because the non - random pattern of residuals shows that a non - linear model would be more appropriate.

Answer:

Residual Plot 1: Scatter Plot A, Good fit Residual Plot 2: Scatter Plot B, Good fit (horizontal line) Residual Plot 3: Scatter Plot C, Poor fit