S Chapter 3.7.3

Consider the following data set: Which of the following is the value of the residual for x=73?

-0.0876

Given a set of ordered pairs (x,y) such that Sx=3.2, Sy=1.3, r=-0.74, find the slope of the regression line of y on x.

-0.301

This question refers to this two-way table: The conditional distribution of lawyers by income status (given by rich, very rich, and filthy rich) is:

67%, 54%, 36%

A set of y-values is transformed (in order to yield a better linear fit) by taking the natural logarithm of each value. The regression of ln y on x is then computed as ln y = -3.1 + 2.5x. What is the predicted value of y when x=3?

81.3

This question refers to the MINITAB output that appears below. The proportion of the variation in y explained by x is:

98.9%

Consider the following scatterplot . The point highlighted, (95, 37), is both an outlier and an influential point. Which of the following best describes what would happen if that point were removed?

Correlation will increase; slope of regression line will decrease

Which of the following statements best describes the scatterplot pictured? I. A linear model appears to be a good fit II. The variables are positively associated III. The variables are negatively associated

I and II only

This question refers to this two-way table: You want to use the data in the table to determine who is richer, doctors or lawyers. Which of the following would be helpful? I. Compare the marginal distribution II. Compare the conditional distributions for doctors and lawyers III. Compare the conditional distributions for rich, very rich, and filthy rich.

II and III only

You're interested in predicting college grades (Y) from SAT scores (X). You know that the mean of the SAT scores is 485 with a standard deviation of 85. The average college GPA is 3.1 within a standard deviation of 0.4. The correlation between SAT scores and college grades is 0.71. What is the equation of the regression of grades (Y) on scores (X)?

y= 1.645 + 0.003x

The following is some MINITAB regression output, but the regression equation has been cut out. What is the regression equation and the correlation coefficient? The regression is __ s=0.2560 R-sq=98.9% R-sq(adj)= 98.8%

y= 64.93 + 0.63x, r=0.994

Which of the following can you conclude from this residual plot?

A straight line is a good model for the relationship between the variables

I measure a response variable y at several values of the explanatory variable x, which is "time of measurement." Plotting log y against time of measurement looks approximately like a straight lie with a positive slope. I can conclude that:

An exponential curve would fit well to a plot of y against time