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Question

phaney Lora à Req?site Fitness-, Learning on Sm pa ? Association for Co. Owi Connect- GitHub a Learn to e de i ce.. ? sore Pream na ? wsu om ca Q Search this courses An limportant 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 data for a manufacturing operation cost, an accountant can estimate the cost associated with a partioular manufacturing volume. Consider the following sample of production volumes and total Production Volume (units) Total Cost ($s) 3,600 450 4,600 550 700 6,000 and bo (to 1 decimal). Complete the estimated regression equation (to 1 decimal) b. What is the variable cost per unit produced (to 1 decimal)? e. Compute the coefficient of determination (to 3 decimals). Note: report i between 0 and 1 What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? d. The company's production schedule shows 500 units must be produced next moeth What is the estimated total cost for this operation (to the nearest whole number)? Check My Work

Explanation / Answer

using excel

data -> data analysis -> regression

result

b0 = 846.6666 ,

b1 = 7.6

y^ = 846.6666 + 7.6 *vol

b)

this is given by b1 = 7.6

c)

r^2 = 0.958689   {see the table in bold letter}

% of variation explained by independent variable = r^2

100 * r^2 = 95.8689 %

d)

vol = 500

y^ =846.6666 + 7.6 *vol

=846.6666 + 7.6 *500

= 4646.6666

SUMMARY OUTPUT Regression Statistics Multiple R 0.979127101 R Square 0.958689879 Adjusted R Square 0.948362349 Standard Error 241.5229458 Observations 6 ANOVA df SS MS F Significance F Regression 1 5415000 5415000 92.82857143 0.00064897 Residual 4 233333.3333 58333.33333 Total 5 5648333.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 846.6666667 464.1599341 1.824083908 0.14220096 -442.0479105 2135.381 -442.048 2135.381 volume 7.6 0.788810638 9.634758504 0.00064897 5.409910566 9.790089 5.409911 9.790089

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