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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. y, quarterly gross revenues ($10,000) = 1.2.....27 Σ y = 7,146 Σty – 113,615 SS-120,640

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. y, quarterly gross revenues ($10,000) = 1.2.....27 Σ y = 7,146 Σty – 113,615 SS-120,640

Linear Algebra: A Modern Introduction
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 33EQ
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X 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 y = 7,146 3ty w 113,615 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). d. Construct 95% prediction intervals for y28 and Y29- e. Construct 90% prediction intervals for y2a and Y20
Transcribed Image Text:X 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 y = 7,146 3ty w 113,615 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). d. Construct 95% prediction intervals for y28 and Y29- e. Construct 90% prediction intervals for y2a and Y20
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