Following data on the demand for sewing machines manufactured by Taylor and Son Co. have been compiled for the past 10 years. Year 1971 1972 1973 1974 1975 78 1976 1977 1978 1979 99 1980 106 Demand 58 65 73 76 87 88 93 in (1000 units) Please estimate the value of demand for next 3 years using trend analysis.

Following data on the demand for sewing machines manufactured by Taylor and Son Co. have been compiled for the past 10 years. Year 1971 1972 1973 1974 1975 78 1976 1977 1978 1979 99 1980 106 Demand 58 65 73 76 87 88 93 in (1000 units) Please estimate the value of demand for next 3 years using trend analysis.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

See similar textbooks

Similar questions

Under what conditions might a firm use multiple forecasting methods?
The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
Apply the 3-years moving average method to forecast the required demand for the year 2021 from the following historical data. Years 2012 2013 2014 2015 2016 2017 2018 2019 2020Demand 245 325 300 350 295 400 450 380 500
Make a 15-year simple linear regression forecast. Example: Railroad Products Co. RPC Sales Car Loadings Year ($millions) (millions) 1 9.5 120 2 11.0 135 3 12.0 130 4 12.5 150 5 14.0 170 6 16.0 190 7 18.0 220
The number of internal disk drives (in millions) made at a plant in Taiwan during the past 5 years follows: Year Disk Drives 1 138 2 156 3 184 4 202 220 a) Using simple linear regression, the forecast for the number of disk drives to be made next year = 211 disk drives (round your response to one decimal place). b) The mean squared error (MSE) when using simple linear regression = drives- (round your response to one decimal place). c) The mean absolute percentage error (MAPE) when using simple linear regression = % (round your response to one decimal %3D place).
Following table shows the weekly sales of smart phones at electronic retail store: Week Number of smart phones Sold Forecast using 3 period moving average Error Forecast using exponential smoothing (with α =0.4) Error 1 48 2 41 3 55 4 64 5 62 6 55 7 8 Answer the following questions based on the data given above. Show all your calculations. What is the expected sales for the 7th week based on 3 period moving average. What is the forecast for the 8th week using the same method if the actual sales for week 7 happens to be 70? What is the expected sales for the 7th week based on exponential smoothing with α = 0.3? Which of the above two forecasting method is better based on MSE? Explain why?
Given the following history, use a three-quarter moving average to forecast the demand for the third quarter of this year. Note: the 1st quarter is Jan, Feb, and Mar; 2nd quarter id Apr. May, Jun; 3rd quarter Jul, Aug, Sep; 4th quarter Oct, Nov, Dec. JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC LAST YEAR 145 165 170 210 220 245 190 180 175 245 270 295 THIS YEAR 145 170 180 235 275 225 What is the forecast for the third quarter?
For the E-Commerce Retail Sales (Million$) data given in the table below, provide estimates from the 1st Quarter (Q1) of 2016 to the 3rd Quarter (Q3) of 2017 by using two models: 1) Single Exponential Smoothing with α=0.3 2) Moving Average with k=2. Calculate MAPE for each model. Quarter Year Actual Q1 2016 86802 Q2 2016 92004 Q3 2016 93795 Q4 2016 124651 Q1 2017 99491 Q2 2017 106590 Q3 2017 108291 Compare two models above (Single Exponential Smoothing and Moving Average) based on their accuracies. Which forecasting method appears to be better? Using the model you choose, provide forecast for the 4th Quarter (Q4) of 2017. Assuming that this model is overestimating, find the actual value of the Q4 of 2017 based on MAPE value. Some residual graphs from the first forecast model (including wider range of E-Commerce Retail Sales data) are given below. What do these graphs tell about the model? Explain each graph.
Given the following history, use a three-quarter moving average to forecast the demand for the third quarter of this year. Note, the 1st quarter is Jan, Feb, and Mar, 2nd quarter Apr, May, Jun; 3rd quarter Jul, Aug, Sep, and 4th quarter Oct, Nov, Dec. JAN FEB MAR APR MAY Last year This year 130 150 155 215 225 155 155 205 220 245 JUN JUL 230 175 250 AUG SEP 165 155 OCT NOV DEC 230 255 280 Forecast for the third quarter
Given the following history, use a three-quarter moving average to forecast the demand for the third quarter of this year. Note that the 1st quarter is Jan, Feb, and Mar; 2nd quarter Apr, May, Jun; 3rd quarter Jul, Aug, Sep; and 4th quarter Oct, Nov, Dec. (Round final answer to a whole number.) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Last year 165 185 200 230 240 265 210 200 195 265 290 315 This year 175 200 165 260 260 200 Forecast for the third quarter