D EXCERSIE MAT DATA 36 18 Grey 451.2 9 Grey Calories 993.1 70 Intensity Color 37 5 Blue 307.9 4 Red 71 598.6 11 Red 415.1 UA W N- 38 797.8 368.3 1 Blue 16 Grey 72 925 15 Grey 39 388.6 7 Blue 652.8 12 Red 73 611.3 11 Red 40 6 866.5 17 Blue 1275.2 20 Grey 74 563.3 5 Blue 947.7 17 Blue 41 7 1162.8 20 Grey 75 1060.2 19 Red 8 622.2 9 Blue 42 452.3 7 Grey 43 76 886.1 15 Grey 670.6 12 Red 664.1 12 Blue 44 760 10 Red 77 601 8 Red 10 477.9 10 Grey 11 884.4 45 250.8 1 Blue 78 726.2 16 Grey 10 Grey 46 494.7 9 Grey 5 Blue 12 349.7 79 504.2 5 Grey 47 13 10 Red 699.4 12 Red 764.4 80 598.4 8 Grey 48 14 407.4 2 Grey 431.1 7 Blue 81 585.6 11 Red 15 432.8 9 Grey 49 463.1 3 Blue 82 409.2 2 Red 964.4 50 16 19 Blue 480.5 9 Grey 51 17 881.7 14 Grey 382.8 8 Grey 83 823.7 15 Red 9 Blue 610.3 18 634.8 52 8 Grey 84 909.8 15 Grey 53 2 Red 85 568 9 Blue 19 391.7 8 Blue 347.2 745.3 54 882.9 17 Red 20 14 Grey 86 728.5 14 Grey 21 1034.9 55 17 Grey 1074.1 18 Blue 87 1210.4 20 Grey 56 6 Red 22 1032.1 16 Grey 485.2 88 610.7 10 Blue 57 16 Blue 23 389.1 2 Grey 926.7 89 58 24 140.9 2 Blue 203.1 3 Red 271 1 Blue 59 25 403.1 8 Blue 471.4 5 Blue 90 392.5 5 Grey 60 667.1 11 Red 91 26 1087.1 20 Blue 61 248.9 174.8 92 27 5 Grey 3 Grey 62 28 912.6 19 Blue 857.7 14 Red 63 782.6 29 445 4 Blue 13 Grey 790.8 64 30 13 Grey 883.5 17 Grey 65 605.3 31 759.1 14 Blue 11 Blue 425.8 8 Grey 66 32 430.5 6 Red 33 888.5 14 Blue 67 965.2 16 Blue 68 34 597.6 10 Blue 940.1 20 Grey 13 Red 69 740.3 14 Red 35 748.4 36 451.2 993.1 18 Grev 70 9 GreyQUESTIONS CONTINUED [Snack Sales] Q23. Which of the following statements is incorrect about the slope of the Advertisement variable (Ads), BAds ? Knickknacks Co. is evaluating store performance for retail distributors of their products Q29. The figure below is a plot of residuals (y-axis) with respect to one of the predictor Management believes that the key factor in retail sales is square footage of display space devoted estimated coefficie coefficient is $0.586K, which means that, when keeping everything else variables. Which of the following statements is true? to the products. They have collected data from 132 stores and hired you to analyze the data, constant, the weekly sales increases by $0.586K per each additional advertisement in local media according to the regression output population (in 10,000's), the number of times that a retailer advertised in local newspapers, and p-value for Excel's default test on the slope for the e slope for the advertisement variable is 0.0042. residual plot weekly sales of Knickknacks (in ckKnacks (in $K) (dependent variable). Residual plots do not exhibit any :. s level. we conclude that the number of advertisements is not obvious sign of violations of linear regression assumptions. At the 516 significance d. At the 1% significance level" we conclude t statistically significant when explaining weekly sales. Q24. At the significance level of 5%, which of the following variables is statistically significant? Ads, Space, Population b. Ads only Population only d. Ads and Space 29 value Ads and population Q25. Is there sufficient evidence th t, when keeping eve everything else the same, the average sales . The residual plot does not show any severe violations on the linear regression assumptions. of Knickknack will increase more than $0.5K (=$500) per additional increase of 10,000 people in . The residual plot shows that only the assumption on constant standard deviation is population at the significance level of 5% (a=0.05)? violated. The residual plot shows that only the assumption that errors are not correlated is violated. a- There exists sufficient evidence that the average sales increase by at least $0.5K per Multiple R d. The residual plot shows both heteroskedasticity and autocorrelation. b. There exists insufficient evidence to conclude that the average sales increase by at least onsted R Square 0.825 So.5K per each additional 10,000 people in the p ooo people in the population. Observations There is no way to determine this from the regression output The sales always increase more than $500 regardless. [Heart Attack Al] A start-up company is using a logistic regression algorithm to predict who is ANDVA likely to get a heart attack within 6 months based on two variables - age (Age) and the average Regression Residual 31 2836.62 22.16); 206.889 6.68E-49 number of sleeping hours (Sleep). In the outcome variable, the chosen response level for a logistic regression model is "Heart Attack" (the other level is called "No Heart Attack".) Using the data from 100 patients, the logistic regression model was fitted and resulted in the 2.607 16.4084 0.1590 g Lower 95% 35.0738 following out Intercept ads 1:2438 3.3362 pace (sq. ft) 2.331 1 0.0632 Population (10.000's) 0.2873 3.8278 0.0015 0.5315 Logistic regression (GLM) response variable : ONECe Atta Q19. Based on the regression output, is the overall model statistically significant at the significant level of 1% (a=0.01)? Explanatory variables: ABC, 2 6% x on Heart_Attack Since the p-value is practically zero, we reject the null hypot [Color of Exercise Mat] Alt. hyp. : there is an effect of x on Heart_Attack . the OR ORX coefficient std. error z. value p. value b. Since the p-value is practically zero, we do not reject the null hypothesis of the F-test. A company claims that people will burn more calories just by using an exercise mat with more (Intercept) 1. 086 8.6% 2: 083 2:017 8:869