1. [-/1 Points] DETAILS ASWESBE9 14.8.015. ASK YOUR TEACHER PRACTICE ANOTHER MY NOTES Consider the data 1012 The estimat
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1. [-/1 Points] DETAILS ASWESBE9 14.8.015. ASK YOUR TEACHER PRACTICE ANOTHER MY NOTES Consider the data 1012 The estimat
4. (-/1 Points] DETAILS ASWESBE9 14.E.021. MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume Total Cost (units) 400 4,100 450 5,100 550 5,300 600 6,000 700 6,300 750 7,000 (a) Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. (Round your numerical values to two decimal places.) P = (b) What is the variable cost (in dollars) per unit produced? (c) Compute the coefficient of determination. (Round your answer to three decimal places.) What percentage of the variation in total cost can be explained by production volume? (Round your answer to one decimal place.) % (d) The company's production schedule shows 500 units must be produced next month. Predict the total cost (in dollars) for thi operation. (Round your answer to the nearest cent.) $ Need Help? Read it Watch it
-[-/1 Points] DETAILS ASWESBE9 14.8.019.MI. MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Salesperson Years of Experience Annual Sales ($1,000s) 1 1 80 2 3 97 3 4 92 4 107 5 6 103 6 B 111 7 10 119 8 10 128 9 11 117 10 13 136 (a) Compute SST, SSR, and SSE. SST - SSR- SSE- (b) Compute the coefficient of determination 2 (Round your answer to three decimal places.) 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 did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. 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. The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. 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. (c) What is the value of the sample correlation coefficient? (Round your answer to three decimal places.)
1. [-/1 Points) DETAILS ASWESBE9 14.E.015. MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER Consider the data. է 5 * 1 2 3 4 3 4 10 12 The estimated regression equation for these data is ý = 0.90 + 2.10x. (a) Compute SSE, SST, and SSR using equations SSE = E(y - 3)2 SST - EY - 73 and SSR = 169,- 72 SSE - SST - SSR = (b) Compute the coefficient of determination ? 2م 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 line. The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. 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. The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
2. (-/1 Points] DETAILS ASWESBE9 14.8.017. MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER Consider the data. * 2 6 9 13 20 Y819 927 22 The estimated regression equation for these data is ý = 9 + 0.8x. What percentage of the total sum of squares can be accounted for by the estimated regression equation? (Round your answer to one decimal place.) % What is the value of the sample correlation coefficient? (Round your answer to three decimal places.) Need Help? Read It