Find the linear regression line for a scatterplot formed by the points (10, 261), (21, 252), (42, 209), (33, 163), and (52, 98).

Unusual points Each of the four scatterplots that follow shows a cluster of points and one “stray” point. For each, answer these questions:
1) In what way is the point unusual? Does it have high leverage, a large residual, or both?
2) Do you think that point is an influential point?
3) If that point were removed, would the correlation be- come stronger or weaker? Explain.
4) If that point were removed, would the slope of the re- gression line increase or decrease? Explain

Sketch a scatterplot where the association is nonlinear, but the correlation is close to r = -1.

Make a scatterplot of the data. Use 87 for 1987.

$\begin{array}{|cc|}\hline \text{ Year }& \text{ Sales }\\ & \text{ (millions of dollars) }\\ 1987& 300\\ 1988& 345\\ 1989& 397\\ 1990& 457\\ 1991& 510\\ 1992& 587\\ 1993& 664\\ 1994& 700\\ 1995& 770\\ 1996& 792\\ 1997& 830\\ 1998& 872\\ 1999& 915\\ \hline\end{array}$

Scatterplots Which of the scatterplots below show
a) little or no association?
b) a negative association?
c) a linear association?
d) a moderately strong association?
e) a very strong association?

Match each verbal statement with the value o fQCR U best matches. Drawing sample scatter plots might help you decide QCR values: -1 0 0.33 0.81 1. a. Mrs. A Every student who was below average on test 1 was also below average on test 2. Every student who was above average on test 1 was also above average on test 2." b. Ms. B: "Most of the students who were below average on test 1 were below average on test 2. Similarly, most of the students scoring above average on test 1 were also above average on test 2. There were a few exceptions, but the trend was clear." c. Mr. C: "Wow, there was really no correlation between test 1 and test 2! half the students who were below average on the first test were also below average on the second test, but half were above average! The same was true for the above average students!" d. Mr. D: "This is so weird. Every student who was below average on test 1 was above average on test 2, and vice versa." e. Mr. E: "Of those scoring above average on test 1, about 60% were above average on test 2, but 40$ were below average."

Predicting Land Value Both figures concern the assessed value of land (with homes on the land), and both use the same data set. (a). Which do you think has a stronger relationship with value of the land-the number of acres of land or the number of rooms n the homes? Why? b. Il you were trying to predict the value of a parcel of land in e arca (on which there is a home), would you be able to te a better prediction by knowing the acreage or the num- ber of rooms in the house? Explain. (Source: Minitab File, Student 12. "Assess.")

Answer true or false to the following statements and explain your answers.
a. In multiple linear regression, we can determine whether we are extrapolating in predicting the value of the response variable for a given set of predictor variable values by determining whether each predictor variable value falls in the range of observed values of that predictor.
b. Irregularly shaped regions of the values of predictor variables are easy to detect with two-dimensional scatterplots of pairs of predictor variables, and thus it is easy to determine whether we are extrapolating when predicting the response variable.