QUESTION 1. Regression analysis was applied between demand for a product (y) and the price of the product (x) that may vary between 1 and 5, and the following estimated regression equation was obtained: y = 120+10x . Based on the above equation, if price is 3 units, the predicted demand
A. increases by 120 units
B. decreases 100 units
C. is 130 units
D. is 150 units
QUESTION 2. In a regression model, which of the following tests is used in order to determine whether an individual independent variable is significant?
A. t test
B. z test
C. F test
D. chi-sqaure test
QUESTION 3. A variable that takes on the values of 0 or 1 and is used to incorporate the effect of categorical variables in a regression model is called
A. an interaction
B. a constant variable
C. a dummy variable
D. none of these alternatives is correct
QUESTION 4. In multiple regression analysis, the correlation among the independent variables is termed
A. homoscedasticity
B. linearity
C. multicollinearity
D. adjusted coefficient of determination
QUESTION 5. Exhibit 7-1. Linear regression analysis was applied between sales data (y in $1,000s) and advertising expenditures (x in $100s). A random sample of 17 observations led to the following information:
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ANOVA
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df
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SS
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MS
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F
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Regression
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1
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225
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225
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45
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Error
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15
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75
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5
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Total
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16
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300
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Coefficients
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Standard Error
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t Stat
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Intercept
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11
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7.454
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Expenditure
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2
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0.2683
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Replacing the above coefficients we get the model as y= 11+2x
Refer to Exhibit 7-1. If $3,000 is spent on advertising, what are the predicted sales?
A. 6011
B. 5410
C. 71000
D. 66,000
QUESTION 6. Refer to Exhibit 7-1. If $500 additional dollars is spent on advertising, then the predicted sales will
A. 9000
B. 10,000
C. 12,000
D. 3600
QUESTION 7. Refer to Exhibit 7-1. __75%_ percent of variations in sales was explained by advertising expenditures.
The percentage of an dependent variable explained by independent variable is the the r square which is equal to SSR/SST = 225/300 =75
A. 75
B. 80
C. 70
D. 85
QUESTION 8. Refer to Exhibit 7-1. The value of the t statistic for testing whether x and y are related is
A. 6.71
B. 7.45
C. 1.96
D. 9.55
QUESTION 9. Refer to Exhibit 7-1. The p-value for testing whether x and y are related is
A. between 0.001 and 0.01
B. between 0.01 and 0.05
C. less than 0.00025
D. more than 0.1
QUESTION 10. Refer to Exhibit 7-1. The 99% confidence interval for the parameter β1 in extends from
1.2288 to 2.3724
1.0090 to 2.5910
1.2093 to 2.7907
0.9492 to 2.6515
QUESTION 11. Exhibit 7-2. A multiple linear regression was used to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars per year. The following results were obtained.
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ANOVA
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DF
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SS
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Regression
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2
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45.9634
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Residual
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11
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2.6218
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Total
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Coefficients
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Standard Errortsig
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Intercept
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0.0136
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- -
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x1
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0.7992
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0.07410.8
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x2
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0.2280
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0.190 1.2
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x3
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-0.5796
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0.920 -0.63
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Refer to Exhibit 7-2. What was the number of families used in this study?
A. 12
B. 13
C. 14
D. 15
QUESTION 12. Refer to Exhibit 7-2. The predicted annual spending of family of size 4 making $90,000 a year, and adding $5,000 annually to their savings is
$58,491
$62,385
$69,956
$75,603
1 points
QUESTION 13. Refer to Exhibit 7-2. If the family addition to savings increases by $2000, and the values of family income and size remain fixed, the predicted annual family spending.
A. increase by $799.2
B. decreases by $1159.2
C. increases by $579.6
D. decreases by $579.6
QUESTION 14. Refer to Exhibit 7-2. The R-square in explaining the variations in family spending explained by family income, size and additions to savings is
A. 94.6
B. 90.7
C. 85.5
D. 83.4
QUESTION 15. Refer to Exhibit 7-2. The value of the F statistic for testing the overall significance of the regression model is
A. 10.75
B. 64.28
C. 50.19
D. 17.21
QUESTION 16. Refer to Exhibit 7-2. The p-value for testing the overall significance of the regression model is
A. less than 0.01
B. between 0.01 and 0.025
C. between 0.025 and 0.05
D. more than 0.05
QUESTION 17. Refer to Exhibit 7-2. At 1% significance level, the conclusion is that the Only x1 has a p -value less than 0.01 so it's the only significant variable at this level of significance
A. model is insignificant
B. model is significant
C. x1 is insignificant
D. x3 is significant
QUESTION 18. Refer to Exhibit 7-2. The value of the t statistic for testing whether the family spending and addition to savings are related is
A. 3.27
B. -0.58
C. -0.63
D. -0.92
QUESTION 19. Refer to Exhibit 7-2. The p-value for testing whether the family spending and addition to savings are related is
A. less than 0.10
B. between 0.10 and 0.20
C. more than 0.40
D. between 0.20 and 0.40
QUESTION 20. Refer to Exhibit 7-2. The 90% confidence interval for the parameter β3 extends from
A. 1.2281 to 2.3726
B. -2.2319 to 1.0727
C. -0.6796 to -0.4796
D. -0.9493 to 2.6510