17.4 Instead of slope, intercept, and standard error, show the final fitted equation and coefficientof determination (R?)** DO repeat the problem with x and y reversed and interpret the results. 17.4 Use least-squares regression to fit a straight line to671115 17 21 2329 29373929 21 29 14 21 15 7 7133Along with the slope and the intercept, compute the standard error ofthe estimate and the correlation coefficient. Plot the data and the re-gression line. If someone made an additional measurement of x = 10,y = 10, would you suspect, based on a visual assessment and thestandard error, that the measurement was valid or faulty? Justify yourconclusion.

Given data - x 6 7 11 15 17 21 23 29 29 37 39 y 29 21 29 14 21 15 7 7 13 0 3

Answered: 17.4 Use least-squares regression to…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 45P

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The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
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A study to determine the correlation between bankdeposits and consumer price indices in Birmingham, Alabama,revealed the following (which was based on n = 5 years of da ta):• LX = 15• Lx2 = 55• Lxy = 70• Ly = 20• L/ = 130a) What is the equation of the least-squares regression line?b) Find the coefficient of correlation. What does it imply to you?c) What is the standard error of the estimate?
4-13 Students in a management science class have just received their grades on the first test. The instructor has provided information about the first test grades in some previous classes, as well as the final averages for the same students. Some of these grades have been sampled and are as follows: STUDENT 1 2 3 4 5 6 7 8 1st test grade 98 77 88 80 96 61 66 95 69 Final average 93 78 84 73 84 64 64 95 76 a. Develop a regression model that could be used to predict the final average in the course based on the first test grade. b. Predict the final average of a student who made an 83 on the first test. c. Give the values of r and r2 for this model. Interpret the value of r² in the context of this problem.
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data table below shows the number of computers sold at the Best Buy Store in a week, based on online ads. Online Ad Computers Sold 2 25 1 10 4 30 1 10 2 25 sigma)x=10 sigma)y=100 draw the graph of the regression equation, showing the slope and y intercepts?

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