1 You have been asked to forecast the sales of your lead product over the next 2 years. You have access to salesfigures for the past 5 years. Using the sales forecasting spreadsheet and the numbers from the past 5 years,shown below, what are the expected sales in year 7?YearUnits Sold1100004512500110001000013000QUESTION 2According to your Average Total Cost Curve (ATC), average production costs per unit in the range of 14,000 unitsper year is $850 per unit. Marketing has decided the price should be $1000 per unit for the next 2 years. What isthe estimated Net Income from the production operations in Year 7, using your answer from question 1 on thisexam?

Sales forecasting is the process of anticipating future revenue by predicting how much goods or…

Answered: You have been asked to forecast the…

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...

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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?
Under what conditions might a firm use multiple forecasting methods?
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?
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period simple moving average method to forecast the number of sales to expect next month. Month Demand Forecast 1 308 341 388 343.3 3 344 364.7 4 400 346.7 341 377.3
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the exponential smoothing method to forecast the number of sales to expect next month. Use the following value of alpha: 0.2. Month Demand Forecast 1 308 349.2 388 340.9 3 344 350.3 4 400 349.1 341 359.3
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 January, February, and March; 2nd quarter April, May, June; 3rd quarter July, August, September; and 4th quarter October, November, December.
1
My App is a small but growing start-up that sees demand for several of its apps increasing quickly. The table below shows the last six months of downloads. Use a forecast for the first month of 220,000, an initial trend forecast of 35,000, and smoothing parameters of 015 for both demand smoothing and trend smoothing Month (t) Monthly Application Downloads Forecast for Next Month Trend 220,000.00 35,000.00 200,000 2 250, 100 334,000 380,000 440,000 500,000 (Round your answers to 2 decimal places.) Complete the table above, filling in the "Forecast for next month" and "Trend" columns, using double a. exponential smoothing (Round your answers to 2 decimal places.) Month 7's forecast is b.
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period weighted moving average method to forecast the number of sales to expect next month. Use the following weights: 0.4 for the most recent, 0.3 for the 2nd most recent, and 0.3 for the 3rd most recent. Month Demand Forecast 1 308 337 388 341.4 3. 344 368 4 400 342.8 341 378.4
Suppose a firm has had the following historic sales figures. Year: 2016 2017 2018 2019 Sales $3,400,000 $4,650,000 $3,300,000 $2,900,000 What would be the forecast for next year's sales using the naïve approach? Answer is complete but not entirely correct. Next year's sales $ 3,550,000 2020 $3,500,000
I just need the first 3 please (:

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