Given the following historical data, what is the moving-average forecast for period 6 based on the last three periods? Period Value 1 73 2 68 3 65 4 72 5 67 (Round your answer to 2 decimal places.) (Round your answer to 2 decimal places.)
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Given the following historical data, what is the moving-average
Period | Value |
1 | 73 |
2 | 68 |
3 | 65 |
4 | 72 |
5 | 67 |
(Round your answer to 2 decimal places.)
(Round your answer to 2 decimal places.)
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- 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?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_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?
- Sales of hair dryers at the Walgreens stores in Youngstown,Ohio, over the past 4 months have been 100, 110, 120, and 130units (with 130 being the most recent sales).Develop a moving-average forecast for next month, usingthese three techniques:a) 3-month moving average.b) 4-month moving average.c) Weighted 4-month moving average with the most recentmonth weighted 4, the preceding month 3, then 2, and theoldest month weighted 1.d) If next month’s sales turn out to be 140 units, forecast the following month’s sales (months) using a 4-month mov-ing average.Given the following historical data, what is the simple three-period moving average forecast for period 6? Period Demand 1 73 2 71 3 72 4 73 66 5 (Keep two decimal places in your answer)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?
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