Spreadsheet Modeling & Decision Analysis: A Practical Introduction To Business Analytics, Loose-leaf Version
8th Edition
ISBN: 9781337274852
Author: Ragsdale, Cliff
Publisher: South-Western College Pub
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The following time series represents the number of automobiles sold by a car dealership each of the past five months.
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(a) Construct a time series plot.
Time Series Value
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Y₁ 7 12 8 15 16
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The Wellington company wants to develop a simple linear regression model for one of its products. Use the following 12 periods of historical data to develop the
regression equation and use it to forecast the next three periods.
Click the icon to view the historical data for the previous 12 periods.
The simple linear regression line is F₁ =
+ x₁. (Enter your responses rounded to two decimal places and include a minus sign if necessary.)
Find the forecasts for periods 13-15 based on the simple linear regression and fill in the table below (enter your responses rounded to two decimal places).
Period
Forecast
(Ft)
Period
(x)
1
2
3
4
5
6
7
8
9
10
11
12
(y)
905
930
825
774
791
647
656
661
479
669
494
441
X
(x) Fo
13
14
15
What is differ from SMA (Simple moving average), WMA (Weighted moving average), SLR (Single linear regression), and ESM ( Exponential smoothing model).
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- The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?arrow_forwardThe file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?arrow_forwardDo the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.arrow_forward
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