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Unformatted text preview: were obtained from a simple regression analysis:
= 37.2895  (1.2024)X
r2= .6744 sb = .2934
____________ is the proportion of the variation explained by the simple linear regression
model.
A. 1.2024
B. .6774
C. 37.2895
D. .2934 11148 Chapter 01  An Introduction to Business Statistics 41. When the constant variance assumption holds, a plot of the residual versus x:
A. Fans out
B. Funnels in
C. Fans out, but then funnels in
D. Forms a horizontal band pattern
E. Suggests an increasing error variance 42. Which of the following is a violation of the independence assumption?
A. Negative autocorrelation
B. A pattern of cyclical error terms over time
C. Positive autocorrelation
D. A pattern of alternating error terms over time
E. All of the above 43. For the same set of observations on a specified dependent variable two different
independent variables were used to develop two separate simple linear regression models. A
portion of the results is presented below. Based on the results given above, we can conclude that:
A. A prediction based on Model 1 is better than a prediction based on Model 2.
B. A prediction based on Model 2 is better than a prediction based on Model 1.
C. There is no difference in the predictive ability between Model 1 and Model 2.
D. There is not sufficient information to determine which of the two models is superior for
prediction purposes. 44. For a given data set, specific value of X, and a confidence level, if all the other factors are
constant, the confidence interval for the mean value of Y will _______ be wider than the
corresponding prediction interval for the individual value of Y.
A. Always
B. Sometimes
C. Never 11149 Chapter 01  An Introduction to Business Statistics 45. The strength of the relationship between two quantitative variables can be measured by:
A. The slope of a simple linear regression equation
B. The Y intercept of the simple linear regression equation
C. The coefficient of correlation
D. The coefficient of determination
E. Both C and D above 46. After plotting the data point s on a scatter diagram, we have observed an inverse
relationship between the independent variable (X) and the dependent variable (Y). Therefore,
we can expect both the sample _____ and the sample _____________ to be negative values.
A. Intercept, slope
B. Slope, coefficient of determination
C. Intercept, correlation coefficient
D. Slope, correlation coefficient
E. Slope, standard error of estimate 11150 Chapter 01  An Introduction to Business Statistics 47. Regression Analysis The local grocery store wants to predict the daily sales in dollars. The manager believes that
the amount of newspaper advertising significantly affects the store sales. He randomly selects
7 days of data consisting of daily grocery store sales (in thousands of dollars) and advertising
expenditures (in thousands of dollars). The Excel/MegaStat output given above summarizes
the results of the regression model.
What is the estimated simple linear regression equation?
A.
= 7.9682 + 1.667 X
B. = 63.333 + 6.667 X C.
D. = 7.948 + 4.000 X
= 11.547 + 1.667 X E. = 6.667 + 63.333 X 11151 Chapter 01  An Introduction to Business Statistics 48. Regression Analysis The local grocery store wants to predict the daily sales in dollars. The manager believes that
the amount of newspaper advertising significantly affects the store sales. He randomly selects
7 days of data consisting of daily grocery store sales (in thousands of dollars) and advertising
expenditures (in thousands of dollars). The Excel/MegaStat output given above summarizes
the results of the regression model.
At a significance level of .05, test the significance of the slope and state your conclusion.
A. We reject H0 and conclude there is sufficient evidence that dollars spent on advertising is a
useful linear predictor of the grocery store sales.
B. We failed to reject H0 and conclude there is not sufficient evidence that dollars spent on
advertising is a useful linear predictor of the grocery store sales.
C. We failed to reject H0 and conclude there is sufficient evidence that dollars spent on
advertising is a useful linear predictor of the grocery store sales.
D. We reject H0 and conclude that there is sufficient evidence that grocery store sales in
dollars is a useful linear predictor of the dollars spent on advertising.
E. We reject H0 and conclude that there is not sufficient evidence that dollars spent on
advertising is a useful linear predictor of the grocery store sales. 11152 Chapter 01  An Introduction to Business Statistics 49. Regression Analysis The local grocery store wants to predict the daily sales in dollars. The manager believes that
the amount of newspaper advertising significantly affects the store sales. He randomly selects
7 days of data consisting of daily grocery store sales (in thousands of dollars) and advertising
expenditures (in thousands of dollars). The Excel/MegaStat output given above summarizes
the results of the regression model.
What is the value of the simpl...
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 Winter '14

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