13. What proportion of the variance in Course % is attributable to the regression model? A- .568 B- .322 C- 960.521 D- 22.822 14. What is the regression equation for this analysis? A. Y=55.423x+.370 B. Y=55.423r+6.331 C. Y=.370x+55.423 D. Y=.370x+.077
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13. What proportion of the variance in Course % is attributable to the regression model?
A- .568
B- .322
C- 960.521
D- 22.822
14. What is the regression equation for this analysis?
A. Y=55.423x+.370
B. Y=55.423r+6.331
C. Y=.370x+55.423
D. Y=.370x+.077
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- 2You conducted a regression analysis between the number of absences and number of tasks missed by your 5 classmates in Statistics and Probability. It resulted that the regression line is y = 0.65x + 1.18. What is the predicted number of tasks missed of a learner who is always present? a. The learner has 1 task missed. b. The learner has less than 2 tasks missed. c. The learner has more than 2 tasks missed. d. The learner has no task missed.The sweetness, y, of the fruit is supposed to be related to the average daily sunshine hours, x. The following data shows the sweetness of the same type of fruit at different locations (sunshine hours). Fit the data to a simple linear regression model. x: 5, 6, 7, 6, 6, 8, 7, 5. y: 9, 10, 10, 11, 12, 13, 12, 8. Predict the true mean sweetness for average daily hours of 8 hours, and calculate the residual for average daily sunshine hours of 8 hours.
- Why was the variable “# of customer service representatives” dropped from the model? Write the regression equation. Sales of men’s clothing (predicted) = Are the regression coefficients significantly different from 0? If one mails 10,000 catalogs and has 15 phone lines open, what would the predicted sales of men’s clothing be? How would you interpret the regression coefficient for number of catalogs mailed? What is the final R2 of the model? How would you interpret this? Which of the two independent variables is the most important predictor of the dependent variable? Why?I need help finding the answers for A,B,and CSuppose that you run a correlation and find the correlation coefficient is -0.281 and the regression equation is y = 27.68 2.2x. The mean for the data values was 4.9, and the mean for the Y data values was 17. AT Test for the slope of the regression line is performed, and the p-value is greater than the level of significance of 0.05. Use the appropriate method to predict the y value when x is 2.3.
- The correlation between X and Y is -.27, and the regression equation is Ŷ = - 0.49X + 9.27 before the data are standardised. A: What is the correlation after the data are standardised? B: What would be the standardised regression equation? A: .27, B: ?̂y = .27?x A: -.49, B: ?̂y = - .49?x + 9.27 A: -.27, B: ?̂y = - .27?x A: -.49, B: ?̂y = - .49?xThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to ExcelIncrease in sales (percent) An advertising firm wishes to demonstrate to its clients the effectiveness of the advertising campaigns it has conducted. The following bivariate data on twelve recent campaigns, including the cost of each campaign (in millions of dollars) and the resulting percentage increase in sales following the campaign, were presented by the firm. Based on these data, we would compute the least-squares regression line to be y = 6.16+0.18x, with x representing campaign cost and y representing the resulting percentage increase in sales. (This line is shown below, along with a scatter plot of the data.) Increase in sales, y Campaign cost, x (in millions of dollars) (percent) 3.02 6.91 7.2+ 1.92 6.80 3.83 6.85 6.8- 1.40 6.37 6.6 - 3.12 6.42 3.56 6.82 6.4- 4.06 6.94 6.2 1.64 6.56 6- 2.06 6.50 1.62 6.18 1.5 2.5 3.5 6.66 Campaign cost (in millions of dollars) 2.87 2.25 6.61 Send data to calculator Based on the firm's data and the regression line, complete the following. (a)…
- If a correlation isr= 0.00, then SP = 0 and the regression equation is O Ý =X +0 O Ý =X + X O Ý =0 + X O Ý =Ÿ + 0The coefficient of correlation in a simple regression analysis is = -0.6. The coefficient of determination for this regression would be 0.36 - 0.36 0.6 0.13 O 0.6 or + 0.64. Consider the following multiple regression results, where the dependent variable is the number of movie tickets sold per week, X, is the ticket price, and X2 is the cost of DVD rental. -51.0918X¡1 + 41.4607X¡2 (37.0184) (13.130) (0.584) Sp t = (-6.800) SSR = 17,023 SSE = 6,262 = 23,285 SST = п 3D 20 Complete the missing entries in the output. 1 | Are the slope coefficients, b, and b2, individually statistically significant (a = 0.10 2. 3. Calculate the standard error of the regression (s.) and the R2.