Regression analysis was applied between sales (y, in dollars) and advertising (x, in dollars) across all the branches of a major international corporation. The following regression function was obtained, y = 5000 + 7.25x. If the advertising budgets of two branches f corporation differ by 30,000, then what will be the predicted difference in their sales? a. 217,500 b. 222,500 c. 5,000 d. 7.25

Regression analysis was applied between sales (y, in dollars) and advertising (x, in dollars) across all the branches of a major international corporation. The following regression function was obtained, y = 5000 + 7.25x. If the advertising budgets of two branches f corporation differ by 30,000, then what will be the predicted difference in their sales? a. 217,500 b. 222,500 c. 5,000 d. 7.25

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

See similar textbooks

Similar questions

Q5. Find the equation of Regression line for the given data. X: 15 3 4 7 Y: 5 6 10 20 2 Find the value of Y when X = 6.
A park has kept the number of visitors since its opening in January. For the first six months of the year, the following numbers were recorded: Month Number of visitors Month Number of visitors January 133 April 640 February 183 May 1,879 March 285 June 2,550 Estimate the intercept and slope of the linear regression equation for forecasting the numbers of visitors. What are the forecasts for July through December of the year using the regression equation? The numbers of visitors to the park were 2,150 in July, and 2,660. Use Holt’s method with α=0.15 and β= 0.10 to find the one-step-ahead and two-step-ahead forecasts for September and October, respectively.
For the provided data, develop a regression model for overall satisfaction as a function of years of service and department that has the largest R. Note that the categorical variable department has multiple levels and will require the use of multiple dummy variables. Which department, if any, has the highest impact on roduction satisfaction? Production Production E Click the icon to view the job satisfaction data. Determine the regression model for overall satisfaction as a function of years of service and department that has the largest R. Let "Administrative" be the baseline department, let X, represent Maintenance, let X, represent Management, let X, represent Production, let X, represent Quality Control, and let Xg represent Shipping / Receiving, coding each department variable with a 1 if the person is in that department and 0 otherwise. In addition, let Xe represent Years. Enter the terms of the equation so that the X-values are in ascending numerical order by base. Select the…
The following data show the brand, price (S), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.522 + 0.335x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 78 B 150 73 95 63 70 58 E 70 38 35 26 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination rt. (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a small proportion of the variability in y…
biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? a) Interpret the slope of the regression line.
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.525 + 0.325x, where x = price ($) and y overall score. %3D Brand Price ($) Score 180 78 B. 150 71 C 95 59 70 54 E 70 40 35 28 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST SSR %3D SSE = (b) Compute the coefficient of determination . (Round your answer to three decimal places.) 1 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y…
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 22.458 + 0.325x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 78 B 150 69 C 95 63 D 70 54 E 70 40 F 35 26 (a) Compute SST (Total Sum of Squares), SSR (Regression Sum of Squares), and SSE (Error Sum of Squares). (Round your answers to three decimal places.) SST=SSR=SSE= (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares…
A student used multiple regression analysis 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. The following results were obtained.
Please solve what’s not filled in.
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
The table shows the total square footage (in billions) of retailing space at shopping centers and their sales (in billions of dollars) for 10 years. The equation of the regression line is y = 516.388x - 1699.161. Complete parts a and b. Total Square Footage, X Sales, y 4.9 5.1 5.2 5.4 5.6 5.7 ********** (a) Find the coefficient of determination and interpret the result. 5.9 5.8 (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? 5.9 883.3 937.6 986.3 1059.5 1107.2 1200.9 1281.1 1340.8 1438.2 1536. 6.2 OA. The coefficient of determination is the fraction of the variation in sales that can be explained by the variation in total square footage. The remaining fraction of the variation is unexplained and is due to other factors or to sampling error. OB. The coefficient of determination is the fraction of the variation in sales that is unexplained and is due to other factors or sampling error. The remaining fraction of the variation is…
Using the weights (Ib) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58 9-0.00749x, where x represents weight Complete parts (a) through (d). Click the icon to view the car data. 東 b. What are the specific values of the slope and y-intercept of the regression line? O A. The slope is 58.9 and the y-intercept is 0.007499. B. The slope is -0.00749 and the y-intercept is 58.9. O C. The slope is 58.9 and the y-intercept is -0.00749. O D. The slope is 0.00749 and the y-intercept is 58.9. c. What is the predictor variable? O A. The predictor variable is highway fuel consumption, which is represented by x. B. The predictor variable is weight, which is represented by x. O C. The predictor variable is weight, which is represented by y O D. The predictor variable is highway fuel consumption, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel…