AP Stats Chapter 6

A LSRL for predicting weights of basketball players on the basis of their heights produced the residual plot below. What does the residual plot tell you about the linear model? (click on the picture to enlarge)

• A residual plot is not an appropriate means for evaluating a linear model.

• The curved pattern in the residual plot suggests that there is no association between the weight and height of basketball players.

• The curved pattern in the residual plot suggests that the linear model is not appropriate.

• There are not enough data points to draw any conclusions from the residual plot.

• The linear model is appropriate, because there are approximately the same number of points above and below the horizontal line in the residual plot.

A community college announces that the correlation between college entrance exam grades and scholastic achievement was found to be –1.08. On the basis of this you would tell the college that

• the entrance exam is a good predictor of success

• the exam is a poor predictor of success

• students who do best on this exam will be poor students

• students at this school are underachieving

• the college should hire a new statistician (like Mr. Z)

If bivariate data set A has correlation r = 0.95, and a second bivariate data set B has correlation r = –0.95, then

• a linear model does a better job of describing the relationship between the variables in data set A than in data set B.

• a linear model does a better job of describing the relationship between the variables in data set B than in data set A.

• a linear model is the best description of the relationship between the variables in both data set A and data set B.

• you can’t tell whether either data set is best described by a linear model until you examine the scatterplots.

• a mistake has been made—r cannot be negative.

A copy machine dealer has data on the number of copy machines x at each of 89 customer locations and the number of service calls in a month y at each location. Summary calculations give x̅= 8.4, sx= 2.1, y̅= 14.2, sy= 3.8, and r = 0.86. What is the slope of the least-squares regression line of number of service calls on number of copiers?

• 0.86

• 1.56

• 0.48

• 2.82

• cannot tell from the information given

Suppose we fit a least-squares regression line to a set of data. What is true if a plot of the residuals shows a curved pattern?

• a straight line is not a good model for the data

• the correlation must be 0

• the correlation must be positive

• outliers must be present

• The regression line might or might not be a good model for the data, depending on the extent of the curve.

What % of the area in a Normal curve is within 1 standard deviation of the mean?

• 50%

• 68%

• 95%

• 99.7%

What % of the Normal curve is greater than 1 standard deviation above the mean?

• 68%

• 34%

• 16%

• 5%

Again, assume the test scores follow the model N(82,5). What score would equate to z = 1.6?

• 90

• 83.6

• 87.6

• 74

Again, assume the test scores follow the model N(82,5). What percent of the scores fall below 82?

• 68%

• 50%

• 34%

• 95%

Again, assume the test scores follow the model N(82,5). What percent of the students scored below a 75?

• 8%

• 5%

• less than 1%

• 1.4%