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3.29 The quarterly revenues of a small medical supply company for the last several years (27 quarters) were plotted and seen to have a roughly linear trend component. The information below was computed for y₁, Y2.. Y27 using one of the company's spreadsheet computer programs. Y₁ = t = 1,2,..., 27 = quarterly gross revenues ($10,000) Σ ty = 113,615 E y = 7,146 SS, = 120,640 a. Using the method of least squares, fit a linear trend model to the revenue data. b. Test for significance of the trend component assuming indepen- dent, normally distributed error terms. c. Forecast revenues for t = 28 and t 29 using the model fit in part (a).

3.29 The quarterly revenues of a small medical supply company for the last several years (27 quarters) were plotted and seen to have a roughly linear trend component. The information below was computed for y₁, Y2.. Y27 using one of the company's spreadsheet computer programs. Y₁ = t = 1,2,..., 27 = quarterly gross revenues ($10,000) Σ ty = 113,615 E y = 7,146 SS, = 120,640 a. Using the method of least squares, fit a linear trend model to the revenue data. b. Test for significance of the trend component assuming indepen- dent, normally distributed error terms. c. Forecast revenues for t = 28 and t 29 using the model fit in part (a).

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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3.29 The quarterly revenues of a small medical supply company for the last several years (27 quarters) were plotted and seen to have a roughly linear trend component. The information below was computed for y₁, Y2.. Y27 using one of the company's spreadsheet computer programs. ../ t = 1,2,..., 27 Y, = quarterly gross revenues ($10,000) Σ γ = 7,146 Σ ty = 113,615 SSw = 120,640 a. Using the method of least squares, fit a linear trend model to the revenue data. b. Test for significance of the trend component assuming indepen- dent, normally distributed error terms. c. Forecast revenues for t = 28 and t 29 using the model fit in part (a). d. Construct 95% prediction intervals for y28 and Y29. e. Construct 90% prediction intervals for y28 and Y/29-
Transcribed Image Text:3.29 The quarterly revenues of a small medical supply company for the last several years (27 quarters) were plotted and seen to have a roughly linear trend component. The information below was computed for y₁, Y2.. Y27 using one of the company's spreadsheet computer programs. ../ t = 1,2,..., 27 Y, = quarterly gross revenues ($10,000) Σ γ = 7,146 Σ ty = 113,615 SSw = 120,640 a. Using the method of least squares, fit a linear trend model to the revenue data. b. Test for significance of the trend component assuming indepen- dent, normally distributed error terms. c. Forecast revenues for t = 28 and t 29 using the model fit in part (a). d. Construct 95% prediction intervals for y28 and Y29. e. Construct 90% prediction intervals for y28 and Y/29-
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