Question 1

If SSR is 1800 and SSE is 200, then R2 is .90.

Accepted Answer

A) True 
 B)False

Question 2

In a sample of n = 36, the Student's t test statistic for a correlation of r = -.450 would be:

Accepted Answer

A) -2.110 
B) -2.938 
C) -2.030 
D) can't say without knowing α. 
A) -2.110 
B) -2.938 
C) -2.030 
D) can't say without knowing α.

Question 3

A researcher's results are shown below using n = 25 observations. $$\begin{array} { l c r } 
\hline \text { Variable } & \text { Coefficient } & \text { Standard Error } \
\hline \text { Intercept } & 343.619889 & 61.0823514 \
\text { Slope } & - 2.2833659 & 0.99855319 \
\hline
\end{array}$$ The 95 percent confidence interval for the slope is:

Accepted Answer

A) [-3.282, -1.284]. 
B) [-4.349, -0.217]. 
C) [1.118, 5.026]. 
D) [-0.998, +0.998]. 
A) [-3.282, -1.284]. 
B) [-4.349, -0.217]. 
C) [1.118, 5.026]. 
D) [-0.998, +0.998].

Question 4

If the residuals violate the assumption of autocorrelation, we know that they:

Accepted Answer

A) are probably outliers. 
B) are not independent. 
C) have constant variance. 
D) are normally distributed. 
A) are probably outliers. 
B) are not independent. 
C) have constant variance. 
D) are normally distributed.

Question 5

A local trucking company fitted a regression to relate the travel time (days) of its shipments as a function of the distance traveled (miles). The fitted regression is Time = -7.126 + .0214 Distance, based on a sample of 20 shipments. The estimated standard error of the slope is 0.0053. Find the critical value for a right-tailed test to see if the slope is positive, using α = .05.

Accepted Answer

A) 2.101 
B) 2.552 
C) 1.960 
D) 1.734 
A) 2.101 
B) 2.552 
C) 1.960 
D) 1.734

Question 6

A researcher's results are shown below using Femlab (labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 U.S. states. $$\begin{array} { l c r c c } 
\hline \text { Source of variation } & d f &  { \text { SS } } & M S & F \
\hline \text { Regression } & 1 & 5377.836 & 5377.836 & 5.228879 \
\text { Residual } & 48 & 49367.389 & 1028.487 & \
\text { Total } & 49 & 54745.225 & & \
\hline
\end{array}$$ What is the R2 for this regression?

Accepted Answer

A) .9018 
B) .0982 
C) .8395 
D) .1605 
A) .9018 
B) .0982 
C) .8395 
D) .1605

Question 7

In the model Sales = 268 + 7.37 Ads (both variables in dollars) an additional $1 spent on ads will increase sales by 7.37 percent.

Accepted Answer

A) True 
 B)False

Question 8

The error term εi in the population regression model yi = β0 + β1 xi + εi is assumed to be:

Accepted Answer

A) observable. 
B) autocorrelated. 
C) heteroscedastic. 
D) normally distributed. 
A) observable. 
B) autocorrelated. 
C) heteroscedastic. 
D) normally distributed.

Question 9

In simple linear regression, the coefficient of determination (R2) is estimated from sums of squares in the ANOVA table.

Accepted Answer

A) True 
 B)False

Question 10

A regression was estimated using these variables: Y = annual value of reported bank robbery losses in all U.S. banks ($ millions), X = annual value of currency held by all U.S. banks ($ millions), n = 100 years (1912 through 2011). We would not anticipate:

Accepted Answer

A) autocorrelated residuals due to time-series data. 
B) heteroscedastic residuals due to the wide variation in data magnitudes. 
C) nonnormal residuals due to skewed data as bank size increases over time. 
D) a negative slope because banks hold less currency when they are robbed. 
A) autocorrelated residuals due to time-series data. 
B) heteroscedastic residuals due to the wide variation in data magnitudes. 
C) nonnormal residuals due to skewed data as bank size increases over time. 
D) a negative slope because banks hold less currency when they are robbed.

Question 11

Find the slope of the simple regression $\hat { y }$ = b0 + b1x. $$\begin{array} { | c | c | } 
\hline X & Y \
\hline 3 & 8 \
\hline 7 & 12 \
\hline 5 & 13 \
\hline 9 & 10 \
\hline 11 & 17 \
\hline 13 & 23 \
\hline 19 & 39 \
\hline 21 & 38 \
\hline
\end{array}$$

Accepted Answer

A) 1.976 
B) 3.294 
C) 0.762 
D) -2.228 
A) 1.976 
B) 3.294 
C) 0.762 
D) -2.228

Question 12

Two-tailed t-tests are often used because any predictor that differs significantly from zero in a two-tailed test will also be significantly greater than zero or less than zero in a one-tailed test at the same α.

Accepted Answer

A) True 
 B)False

Question 13

Correlation analysis primarily measures the degree of the linear relationship between X and Y.

Accepted Answer

A) True 
 B)False

Question 14

When X is farther from its mean, the prediction interval and confidence interval for Y become wider.

Accepted Answer

A) True 
 B)False

Question 15

Which is not correct regarding the estimated slope of the OLS regression line?

Accepted Answer

A) It is divided by its standard error to obtain its t statistic. 
B) It shows the change in Y for a unit change in X. 
C) It is chosen so as to minimize the sum of squared errors. 
D) It may be regarded as zero if its p-value is less than α. 
A) It is divided by its standard error to obtain its t statistic. 
B) It shows the change in Y for a unit change in X. 
C) It is chosen so as to minimize the sum of squared errors. 
D) It may be regarded as zero if its p-value is less than α.

Question 16

If SSE is near zero in a regression, the statistician will conclude that the proposed model probably has too poor a fit to be useful.

Accepted Answer

A) True 
 B)False

Question 17

A fitted regression Profit = 262 + 1.51 Sales (all variables in thousands of dollars) was estimated from a random sample of 15 small coffee kiosks. We can say that:

Accepted Answer

A) the slope is too small to be significant. 
B) the intercept does not seem reasonable. 
C) the R2 will be low due to small sample size. 
D) predictions are likely to be underestimated. 
A) the slope is too small to be significant. 
B) the intercept does not seem reasonable. 
C) the R2 will be low due to small sample size. 
D) predictions are likely to be underestimated.

Question 18

Mary used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). Her estimated regression equation was Crime = 428 + 0.050 Income. If Income decreases by 1000, we would expect that Crime will:

Accepted Answer

A) increase by 428. 
B) decrease by 50. 
C) increase by 500. 
D) remain unchanged. 
A) increase by 428. 
B) decrease by 50. 
C) increase by 500. 
D) remain unchanged.

Question 19

Using the least squares formulas, the regression line must pass through the origin.

Accepted Answer

A) True 
 B)False

Question 20

Confidence intervals for predicted Y are less precise when the residuals are very small.

Accepted Answer

A) True 
 B)False

If SSR is 1800 and SSE is 200, then R² is .90.

In a sample of n = 36, the Student's t test statistic for a correlation of r = -.450 would be:

A researcher's results are shown below using n = 25 observations. Variable Coefficient Standard Error Intercept 343.619889 61.0823514 Slope -2.2833659 0.99855319 The 95 percent confidence interval for the slope is:

If the residuals violate the assumption of autocorrelation, we know that they:

In the model Sales = 268 + 7.37 Ads (both variables in dollars) an additional $1 spent on ads will increase sales by 7.37 percent.

The error term ε_i in the population regression model y_i = β₀ + β₁ x_i + ε_i is assumed to be:

In simple linear regression, the coefficient of determination (R²) is estimated from sums of squares in the ANOVA table.

A regression was estimated using these variables: Y = annual value of reported bank robbery losses in all U.S. banks ($ millions), X = annual value of currency held by all U.S. banks ($ millions), n = 100 years (1912 through 2011). We would not anticipate:

Find the slope of the simple regression $\hat { y }$ = b₀ + b₁x. X Y 3 8 7 12 5 13 9 10 11 17 13 23 19 39 21 38

Two-tailed t-tests are often used because any predictor that differs significantly from zero in a two-tailed test will also be significantly greater than zero or less than zero in a one-tailed test at the same α.

Correlation analysis primarily measures the degree of the linear relationship between X and Y.

When X is farther from its mean, the prediction interval and confidence interval for Y become wider.

Which is not correct regarding the estimated slope of the OLS regression line?

If SSE is near zero in a regression, the statistician will conclude that the proposed model probably has too poor a fit to be useful.

A fitted regression Profit = 262 + 1.51 Sales (all variables in thousands of dollars) was estimated from a random sample of 15 small coffee kiosks. We can say that:

Using the least squares formulas, the regression line must pass through the origin.

Confidence intervals for predicted Y are less precise when the residuals are very small.

Overview of Statistics

Data Collection

Describing Data Visually

Descriptive Statistics

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions and Estimation

One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests

Analysis of Variance

Multiple Regression

Time Series Analysis

Chi-Square Tests

Nonparametric Tests

Quality Management

Filters

Exam 12: Simple Regression

If SSR is 1800 and SSE is 200, then R2 is .90.

In a sample of n = 36, the Student's t test statistic for a correlation of r = -.450 would be:

A researcher's results are shown below using n = 25 observations. Variable Coefficient Standard Error Intercept 343.619889 61.0823514 Slope -2.2833659 0.99855319 The 95 percent confidence interval for the slope is:

If the residuals violate the assumption of autocorrelation, we know that they:

In the model Sales = 268 + 7.37 Ads (both variables in dollars) an additional $1 spent on ads will increase sales by 7.37 percent.

The error term εi in the population regression model yi = β0 + β1 xi + εi is assumed to be:

In simple linear regression, the coefficient of determination (R2) is estimated from sums of squares in the ANOVA table.

A regression was estimated using these variables: Y = annual value of reported bank robbery losses in all U.S. banks ($ millions), X = annual value of currency held by all U.S. banks ($ millions), n = 100 years (1912 through 2011). We would not anticipate:

Find the slope of the simple regression y^\hat { y }y^​ = b0 + b1x. X Y 3 8 7 12 5 13 9 10 11 17 13 23 19 39 21 38

Two-tailed t-tests are often used because any predictor that differs significantly from zero in a two-tailed test will also be significantly greater than zero or less than zero in a one-tailed test at the same α.

Correlation analysis primarily measures the degree of the linear relationship between X and Y.

When X is farther from its mean, the prediction interval and confidence interval for Y become wider.

Which is not correct regarding the estimated slope of the OLS regression line?

If SSE is near zero in a regression, the statistician will conclude that the proposed model probably has too poor a fit to be useful.

A fitted regression Profit = 262 + 1.51 Sales (all variables in thousands of dollars) was estimated from a random sample of 15 small coffee kiosks. We can say that:

Using the least squares formulas, the regression line must pass through the origin.

Confidence intervals for predicted Y are less precise when the residuals are very small.

Overview of Statistics

Data Collection

Describing Data Visually

Descriptive Statistics

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions and Estimation

One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests

Analysis of Variance

Multiple Regression

Time Series Analysis

Chi-Square Tests

Nonparametric Tests

Quality Management

Filters

If SSR is 1800 and SSE is 200, then R² is .90.

The error term ε_i in the population regression model y_i = β₀ + β₁ x_i + ε_i is assumed to be:

In simple linear regression, the coefficient of determination (R²) is estimated from sums of squares in the ANOVA table.

Find the slope of the simple regression $\hat { y }$ = b₀ + b₁x. X Y 3 8 7 12 5 13 9 10 11 17 13 23 19 39 21 38