1. In this lesson, you will create residual plots to help you visually determine how well a LSRL fits the data. Day 1

2. 6-47. In problem 6-1, you completed an investigation that helped Robbie use a viewing tube to see a football game. Typical data is shown in the table below. a)Sketch the graph on your paper. What is the equation of the LSRL? b)When entering the data in her calculator, Amy accidentally entered (144, 10.7) for the first data point. Make this change to your data and sketch the new point and new LSRL in a different color. Would you consider this point an outlier? c)What is the impact of the outlier? Will Amy’s predictions for the field of view be too large or too small? How do you know?

3. 6-48. Giulia’s father would like to open a restaurant, and is deciding how much to charge for the toppings on pizza. He sent Giulia to eight different Italian restaurants around town to find out how much they each charge. Giulia returned with the following information: a)Giulia needs to write a report for her father about what he should charge for a two-topping pizza. Discuss with your team what elements a statistical analysis report should contain. b)Write the report you described in part (a), and predict what Giulia’s father should charge for a two- topping pizza.

4. 6-49. A residual plot can help you determine if a linear model is a good fit for a scatterplot of data. Whenever a LSRL line is drawn on a scatterplot, a residual plot can also be created. A residual plot has an x-axis that is the same as the scatterplot, and a y-axis that plots the residual. a)Match each scatterplot below with its corresponding residual plot. b)For which of the scatterplots does a linear model fit the data best? How does the residual plot help you make that decision?

5. 6-50. Help Giulia analyze the residuals for the pizza parlors in problem 6-48 as described below. a)Mark the residuals on the scatterplot you created in problem 6-48. If you want to purchase an inexpensive pizza, should you go to a store with a positive or negative residual? b)What is the sum of the residuals? Are you surprised at this result? c)Your teacher will show you how to make a residual plot using your calculator, with the x-axis representing the number of pizza toppings, and the y-axis representing the residuals. Interpret the scatter of the points on the residual plot. Is a linear model a good fit for the data?