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Lexon Inc. is a large manufacturer of affordable DVD players. Management recently became aware of rising expenses resulting from returns of malfunctioning products. As a starting point for further analysis, Paige Jennings, the controller, wants to test different forecasting methods and then use the best one to forecast quarterly expenses for 2019. The relevant quarterly data for the previous three years follow 2017 Return 2018 Return Quarter Expenses Quarter Expenses Quarter Expenses 14,100 1 $ 14,500 13,400 13,200 15,900 2016 Return 1 $ 13,750 2 12,400 2 12,700 3 15,800 13,200 2 12,800 3 15, 300 4 4 4 The result of a simple regression analysis using all 12 data points yielded an intercept of $13,025.76 and a coefficient for the independent variable of $127.45. (R-squared 0.41, SE $1,066.52.) 2. Looking at the graph you prepared for requirement 1, select two representative data points and calculate the quarterly forecast for 2019 using the high-low method 3. Calculate the quarterly forecasts for 2019 using the results of a regression analysis. Evaluate the results of the regression analysis and make appropriate changes to improve the model.

got number 2 solved

Required 2Required 3 Looking at the graph you prepared for requirement 1, select two representative data forecast for 2019 using the high-low method. Return 2019 Quar Expenses 2 3 4 16,250 16,600 16,950 17,300 Requl Required 3>

now need solution to number 3

Regression One Predicted Expenses 2019 Quarter Regression Two Predicted Expenses 2019 Quarter

fill in the above chart

(similar question and solution below , that's what I need

Cost Estimation; High-Low Method; Regression Analysis Lexon Inc. is a large manufacturer of affordable DVD players. Management recently became aware of rising expenses resulting from returns of malfunctioning products. As a starting point for further analysis, Paige Jennings, the controller, wants to test different forecasting methods and then us the best one to forecast quarterly expenses for 2019. The relevant quarterly data for the previous three years follow: 8-49 2016 Quarter Return Expenses 2017 Quarter Return Expenses 2018 Quarter Return Expenses $12,500 11,300 11,600 13,700 $12,900 12,100 11,700 14,000 $13,300 12,300 12,100 14,600 2 4. The result of a simple regression analysis using all 12 data points yielded an intercept of $11.855.55 and a coefficient for Page 3 the independent variable of $126.22. (R-squared = 0.44, SE = $974.89.) Required Type here to search


The regression equation using a trend variable (1,2,3...) for each of the 12 quarters is as follows: Regression One Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.439734422 0.193366362 0.112702998 974.8928577 12 ANOVA F Significance F 1 2278339. 161 2278339 2.397202 0.152593037 df MS Regression Residual Total 10 9504160.839 950416.1 11 11782500 Intercept Quarter Coefficients Standard Error t Stat P-value Lower 95% 11854. 54545 600.005077 19.75741 2.42E-09 10517.65083 126.2237762 81.52463628 1.54829 0.152593 -55.4244333 Return Expense $11,855 I$126.2238 x (quarter number)] Predicted Expense for the next four quarters using regression analysis: Quarter Regression Prediction 13 $11,855+ (13 x $126.2238)13,495.45 14 $11,855+ (14 x $126.2238) 13,621.68 15 $11,855 + (15 x $126.2238) = $13,747.90 16 $11,855+ (16x $126.2238)$13,874.13 ords

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