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TrimFit manufactures and markets exercise equipment. In 2014 they introduced a new, patented exercise machine. To forecast demand for the product, the marketing department obtained quarterly sales data (in units sold) for 2014 to 2022 (TrimFit worksheet). To fit the seasonality without trend regression model to the data, TrimFit used Quarter 4 as the baselined quarter (the category without an indicator) developed the indicator variable Q1 to represent Quarter 1, i.e., Q1 = 1 for the first quarter of the year and is 0 otherwise. Similarly, Q2 represents Quarter 2 and Q3 represents Quarter 3. of the year. TrimFit used Excel to fit the regression model and the output appears below.

TrimFit manufactures and markets exercise equipment. In 2014 they introduced a new, patented exercise machine. To forecast demand for the product, the marketing department obtained quarterly sales data (in units sold) for 2014 to 2022 (TrimFit worksheet). To fit the seasonality without trend regression model to the data, TrimFit used Quarter 4 as the baselined quarter (the category without an indicator) developed the indicator variable Q1 to represent Quarter 1, i.e., Q1 = 1 for the first quarter of the year and is 0 otherwise. Similarly, Q2 represents Quarter 2 and Q3 represents Quarter 3. of the year. TrimFit used Excel to fit the regression model and the output appears below.

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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39) How will the expected Units (i.e., the point on the fit line) change when going from
the first quarter to the second quarter within a year.
A) Expected Units will stay the same.
B) Expected Units will decrease by 10,000.
C) Expected Units will increase by 5,000.
D) Expected Units will increase by 15,000
E) Expected Units will increase by 7,000.

40) What is the forecast for the expected number of units sold for the year 2023.
A) 356,000
B) 360.000
C) 80,000
D) 90,000
E) 81,000

Use the following scenario for questions 39) and 40): TrimFit manufactures and markets exercise equipment. In 2014 they introduced a new, patented exercise machine. To forecast demand for the product, the marketing department obtained quarterly sales data (in units sold) for 2014 to 2022 (TrimFit worksheet). To fit the seasonality without trend regression model to the data, TrimFit used Quarter 4 as the baselined quarter (the category without an indicator) developed the indicator variable Q1 to represent Quarter 1, i.e., Q1 = 1 for the first quarter of the year and is 0 otherwise. Similarly, Q2 represents Quarter 2 and Q3 represents Quarter 3. of the year. TrimFit used Excel to fit the regression model and the output appears below. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Q1 Q2 Q3 0.8517 0.7254 0.7025 3563.27 40 df 3 36 39 SS MS F 1207500000 4.03E+08 31.7007 457087670 12696880 1664587670 Coefficients Standard Error t Stat 90,000 79.87 -10,000 -6.28 5,000 3.14 -4,000 -2.51 1126.80 1593.54 1593.54 1593.54 Significance F 3.31132E-10 P-value 4.03E-42 2.98E-07 0.003389 0.016703
Transcribed Image Text:Use the following scenario for questions 39) and 40): TrimFit manufactures and markets exercise equipment. In 2014 they introduced a new, patented exercise machine. To forecast demand for the product, the marketing department obtained quarterly sales data (in units sold) for 2014 to 2022 (TrimFit worksheet). To fit the seasonality without trend regression model to the data, TrimFit used Quarter 4 as the baselined quarter (the category without an indicator) developed the indicator variable Q1 to represent Quarter 1, i.e., Q1 = 1 for the first quarter of the year and is 0 otherwise. Similarly, Q2 represents Quarter 2 and Q3 represents Quarter 3. of the year. TrimFit used Excel to fit the regression model and the output appears below. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Q1 Q2 Q3 0.8517 0.7254 0.7025 3563.27 40 df 3 36 39 SS MS F 1207500000 4.03E+08 31.7007 457087670 12696880 1664587670 Coefficients Standard Error t Stat 90,000 79.87 -10,000 -6.28 5,000 3.14 -4,000 -2.51 1126.80 1593.54 1593.54 1593.54 Significance F 3.31132E-10 P-value 4.03E-42 2.98E-07 0.003389 0.016703
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