1) A researcher is curious about the effect of sleep on students' test performances. He chooses 60 students and gives each two tests: one given after two hours' sleep and one after eight hours' sleep. The test the researcher should use would be: A) T-test for the differences in two means assuming equal variances B) T-Test for the differences in two means assuming unequal variances C) Paired t-test D) F test for the differences in variances E) Any of the above --------------------------------------------------------------------------------------------------------------------------2) The amount of time required to reach a customer service representative has a huge impact on customer satisfaction. A study was conducted to see whether there is evidence of a difference in the mean amounts of time required to reach a customer service representative between two hotels. Assume that the population variances in the amount of time for the two hotels are not equal. The 95% confidence interval estimate for the difference in the two means is -0.9543 1 - 2 1.3593. At this level of confidence, this indicates that with respect to amount of time required to reach a customer service representative: A) There is no difference between the two hotels. B) There is a difference between the two hotels with hotel one being slower than hotel two C) There is a difference between the two hotels with hotel two being slower than hotel one D) Not enough information to come to a conclusion ------------------------------------------------------------------------------------------------------------------------------3) In the regression equation, the Y-intercept represents the A) predicted value of Y when X = 0. B) change in estimated Y per unit change in X. C) predicted value of Y. D) variation around the sample regression line. ---------------------------------------------------------------------------------------------------------------------------4) Regression analysis is used for prediction, while correlation analysis is used to measure the strength of the association between two numerical variables. A) TRUE B) FALSE --------------------------------------------------------------------------------------------------------------------------5) The value of r (the correlation coefficient) is always positive. A) TRUE B) FALSE ---------------------------------------------------------------------------------------------------------------------------6) If the correlation coefficient (r) = 1.00, then A) all the data points must fall exactly on a straight line with a slope that equals 1.00. B) all the data points must fall exactly on a straight line with a negative slope. C) all the data points must fall exactly on a straight line with a positive slope. D) all the data points must fall exactly on a horizontal straight line with a zero slope. ----------------------------------------------------------------------------------------------------------------------------- 7) In a simple linear regression problem, r (correlation coefficient) and b1 (slope) A) may have opposite signs. B) must have the same sign. Page 1 of 4 C) must have opposite signs. D) are equal. -----------------------------------------------------------------------------------------------------------------------------8) An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below. SUMMARY OUTPUT Regression Statistics Multiple R 0.991 R Square 0.982 Adjusted R Square 0.976 Standard Error 0.299 Observations 10 ANOVA Regression Residual Total df 2 7 9 Intercept GDP Price Coeff -0.0861 0.7654 -0.0006 SS 33.4163 0.6277 34.0440 MS 16.7082 0.0897 StdError 0.5674 0.0574 0.0028 F 186.325 t Stat -0.152 13.340 -0.219 Signif F 0.0001 p-value 0.8837 0.0001 0.8330 When the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an r2 value of 0.971. What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression? A) 98.2 B) 11.1 C) 2.8 D) 1.1 --------------------------------------------------------------------------------------------------------------------------Use the following information for questions 9 and 10 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: Page 2 of 4 Page 3 of 4 9) Which of the following is the correct interpretation for the slope coefficient? A) For each decrease of 1 thousand downloads, the expected revenue is estimated to increase by $ 3.7297 thousands. B) For each increase of 1 thousand downloads, the expected revenue is estimated to increase by $ 3.7297 thousands. C) For each decrease of 1 thousand dollars in expected revenue, the expected number of downloads is estimated to increase by 3.7297 thousands. D) For each increase of 1 thousand dollars in expected revenue, the expected number of downloads is estimated to increase by 3.7297 thousands. 10) Which of the following assumptions appears to have been violated? A) Normality of error B) Equality of Variances C) Independence of errors D) None of the above --------------------------------------------------------------------------------------------------------------------------- Page 4 of 4