Chapter 12: Simple Regression
6. The standardized residual ei = -2.205 indicates:
a) A rather poor prediction.
b) An extreme outlier in the residuals.
c) A likely data entry error.

Chapter 12: Simple Regression
4. If the attendance at a baseball game is to be predicted by the equation Attendance = 16,500 - 75
Temperature, what would be the predicted attendance if Temperature is 90 degrees?
a) 6,750
b) 9,750
c) 12,250
Y = 16,500 75(9

Chapter 12: Simple Regression
1. A news network stated that a study had found a positive correlation between the number of
children a worker has and his or her earnings last year. You may conclude that:
a) People should have more children so they can get

Chapter 12: Simple Regression
5. Which of the following is not a characteristic of the F-test in a simple regression?
a) It is a test for overall fit of the model.
b) The test statistic can never be negative.
c) The F-test gives a different p-value than t

Chapter 12: Simple Regression
2. Which of the following statements is incorrect?
a) A scatter plot is used to visualize the association (or lack of association) between two
quantitative variables.
b) The correlation coefficient r measures the strength of

Chapter 12 Simple Regression
8. When homoscedasticity exists, we expect that a plot of the residuals versus the fitted Y:
a) Will form approximately a straight line.
b) Crosses the centerline too many times.
c) Will show no pattern at all.

Chapter 12 Simple Regression
7. In a simple regression, which would suggest a significant relationship between X and Y?
a)
b)
c)
d)
Large p-value for the estimated slope
Largest statistic for the slope
Large p-value for the F statistic
Small t-statistic f

Chapter 12 Simple Regression
For the following simple regression output, answer the following questions (below).
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
0.371
0.1377
0.1293
43.95467488
105
Intercept
Si

Chapter 13: Multiple regression
1. Which is not a standard criterion for assessing a multiple regression model?
a) Logic of causation
b) Overall fit
c) Degree of collinearity
d) Binary predictors