QUESTION 9 Consider the following table, which shows revenue of the largest manufacturer of chewing gum, Wrigley Company (partially filled): Year Revenue SMA(5) WMA(3) EWMA(.=0.1) 1984 591 #N/A 1985 620 1986 699 C 1987 781 A 1988 891 B 1989 993 The value of A in the table above is: OA. # N/A O B. 716.4 O C. 796.8 O D. 700 O E. 755.3QUESTION 10 Consider the following table, which shows revenue of the largest manufacturer of chewing gum, Wrigley Company (partially filled): Year Revenue SMA(5) WMA(3) EWMAG.=0.1) 1984 591 #N/A 1985 620 1986 699 C 1987 781 A 1988 891 B 1989 993 When plotting the above data, which heading is referred to as the Y; column? O A. Revenue O B. EWMA( ) = 0.1) O c. Year O D. SMA(5) O E. WMA(3)QUESTION 11 We develop a regression model to predict the assessed value of houses, using the size of the houses (in square feet) and the age of the houses (in years). Below, we observe partial results of running a multiple regression: Multiple R 0.909120107 R Square 0.826499369 Adjusted R Square 0.797582598 Standard Error 2168.165527 Observations 15 ANOVA df SS MS F Regression N 268724699 134362349.5 28.58200688 Residual 12 56411301.01 4700941.751 Total 14 325136000 Coefficients Standard Error t Stat P-value Upper 95% Lower 95.0% Intercept 163775.1236 5407.173152 30.28849253 1.05104E-12 175556.3418 151993.9054 Size 10.72518298 3.014327189 3.558068619 0.003937797 17.29283773 4.157528235 Age -284.254348 83.59835914 -3.400238365 0.005267391 -102.1091708 -466.3995252 Provide the proper interpretation for the slope of the size of houses (m1). O A. For a fixed number of homes, the average size of the houses is predicted to increase by $ 3.55 for every year the houses age. O B. For a fixed number of homes, the average assessed value of the houses will remain the same, given age. O C. For a fixed house age, the average size of the home is predicted to increase by $ 163,775.12 for every year the houses age. O D. For every fixed square footage, the average size of the houses is predicted to decrease by $ 284.25 for every year the houses age. O E. For a fixed house age, the average assessed value of the houses is predicted to increase by $ 10.73 for each increase in square feet.QUESTION 12 To qualify for poverty funds, legislators in a particular district in Philadelphia had to show average household income for a family of four was $ 11,809. A study was commissioned yielding the following: n = 53 households in district x = $ 12,500 annual household income S = $4,320 At o = .10, find the p - value and decide if Ho should be accepted or rejected. O A. Accept Ho: H = $ 11,809 P- value = 6.15% O B. Accept Ho: M = $ 11,809 P- value = 12.3% O C. Accept Ho: H = $ 11,809 P- value = 24.6% O D. Reject Ho: H = $ 11,809 P- value = 24.6% O E. Reject Ho: H = $ 11,809 P- value = 12.3%QUESTION 14 Consider the data below, which compares different teaching methods: Lecture Dramatic Enactments Seminar Case Mix Of Three Methods by Students Discussions Methods n =7 n =7 n =7 n =7 x =25 X = 33 X =31 x =27 $2 = 46 52 = 43 5= = 44 52 = 27 The value of the F test statistic is: O A. 1.82 O B. 2.33 Two Way ANOVA O C. 1.75 O D. 2.99 O E. 1.11 QUESTION 15QUESTION 15 We develop a regression model to predict the assessed value of houses, using the size of the houses (in square feet) and the age of the houses (in years). Below, we observe partial results of running a multiple regression: Multiple R 0.909120107 R Square 0.826499369 Adjusted R. Square 0.797582598 Standard Error 2168.165527 Observations 15 ANOVA dif SS MS F Regression 2 268724699 134362349.5 28.58200688 Residual 12 56411301.01 4700941.751 Total 14 325136000 Coefficients Standard Error t Stat P-value Upper 95% Lower 95.0% Intercept 163775.1236 5407.173152 30.28849253 1.05104E-12 175556.3418 151993.9054 Size 10.72518298 3.014327189 3.558068619 0.003937797 17.29283773 4.157528235 Age -284.254348 83.59835914 -3.400238365 0.005267391 -102.1091708 -466.3995252 In the regression results, which value measures how well the regression equation predicts the assessed values of houses? O A. 0.005 O B. 3.01 O C. 10.73 O D. 0.83 O E. 28.58QUESTION 16 Consider the one-way ANOVA results below, which compare download times of three different types of computers: Anova: Single Factor SUMMARY Groups Count Sum Average Variance MAC 10 1606 160.6 508.0444444 iMAC 10 1831 183.1 188.1 Dell 10 2560 256 214.6666667 ANOVA Source of Variation SS Df MS F P-value F crit Between Groups 49739.4 2 24869.7 81.9150086 3.42516E-12 3.354130829 Within Groups 8197.3 27 303.6037037 Total 57936.7 29 In this instance, the value of k is: O A. 3 O B. 29 O C.2 O D. 27 O E. 10QUESTION 17 Consider the following table, which shows revenue of the largest mmufacmrer of chewing gum, Wrigley Company (partially lled): If the goat 1'5 to smooth the original series, then the value of C in the table is: O A. 648.96 0 B. 622.07 0 0.604.41 0 [1591.00 0 E. 593.90 QUESTION 18 A chef in a restaurant that specializes in pasta dishes was having trouble with getting brands of pasta to be at dame that is, cooked enough so as not to feel starchy or hard. but still feel rm when bitten into. She decided to conduct an. experiment in which two brands of pasta, one American and one Italian, were cooked for either 4 or 8 minutes. The uncooked pasta was added aadthen weighed aiet a given period of time by lilting the pasta from the pot via a [wilt-in strainer. Thedatantamsofweightingramsshete: Four light American 265 310 270 320 Italian 250 300 245 305 And partial mowavANOVA results (an 0.05) arebelow. ANOVA Sam: of Variation SS (f MS F P-valw F cri! Sample 523.125 1 528.125 2-11-1286 0.007966 7.708647 Columns 5253.125 1 5253.125 240.1429 0.000101 7.708647 [am-action 28.125 1 28.125 1.285714 0.320188 7.708647 Within 87.5 4 21.875 Total 5896.875 7 According to the results. what do we determine about the cooking time? 0 A. We conclude that the cooking time results are found in the "within " section. 0 B. We conclude that it becomes inappropriate to consider results for cooking time. O c. We conclude that there is not a significant difference in mean pasta weight when considering cooking time. O D. We conclude that there is a significant difference in mean pasta weight when considering cooking time. Q E. We conclude that the ANOVA pasta results do not provide ample information about cooking time. QUESTION 19 We develop a regression model to predict the assessed value of houses, using the size of the houses (in square feet) and the age of the houses (in years). Below, we observe partial results of running a multiple regression: Multiple R 0.909120107 R Square 0.826499369 Adjusted R. Square 0.797582598 Standard Error 2168.165527 Observations 15 ANOVA df SS MS F Regression N 268724699 134362349.5 28.58200688 Residual 12 56411301.01 4700941.751 Total 14 325136000 Coefficients Standard Error Stat P-value Upper 95% Lower 95.0% Intercept 163775.1236 5407.173152 30.28849253 1.05104E-12 175556.3418 151993.9054 Size 10.72518298 3.014327189 3.558068619 0.003937797 17.29283773 4.157528235 Age -284.254348 83.59835914 -3.400238365 0.005267391 -102.1091708 -466.3995252 At or =0.05, which of the independent variables (size of house and / or age of house) make(s) a significant contribution to the regression model? In other words, which of the independent variables has / have a significant relationship with the dependent variable? O A. Size of house has somewhat of a significant relationship with assessed house value. O B. None of the variables has a significant relationship with assessed house value. O c. Size of house only has a significant relationship with assessed house value. O D. Both size of house and age of house have a significant relationship with assessed house value O E. Age of house only has a significant relationship with assessed house value