Question 16You work for BMW as a planner. BMW is expecting to increase its annual sales for BMW- X5 in year 2021 by 10 % comparedto year 2020 annual sales. Find the quarterly forecast for year 2021, given the following data:(You need to consider the Quarterly seasonal effect)QuarterActual Car Sales (2019)Actual Car Sales (2020)Quarter 18000088000Quarter 2100000110000Quarter 3120000132000Quarter 49000099000000Attach you solutions.Write your results of the 1st Q Forecasted sales in Field.

BMW-X5 sales are increasing by 10% every quarter looking at as per the given data. let us clarify…

Answered: You work for BMW as a planner. BMW is…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 29P: The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building...

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Under what conditions might a firm use multiple forecasting methods?
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
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 file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
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?
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?
Q3) You work for BMW as a planner. BMW sold 1,081,000 of its BMW-X5 cars in 2019 and sold 1,189,100 of its BMW-X5 cars in 2020. BMW is expecting to increase its annual sales for BMW- X5 in year 2021 by 10% compared to year 2020 (total sales volume = 1,308,010). Find the monthly forecast and seasonal indices for year 2021, given the following data: (Please considers the monthly seasonal effect) MONTH Actual Car Sales (2019) Actual Car Sales (2020) Forecasted Car Sales (2021) Jan 80,000 88,000 Feb 85,000 93,500 Mar 86,000 94,600 Apr 90,000 99,000 May 92,000 101,200 June 100,000 110,000 July 120,000 132,000 Aug 90,000 99,000 Sept 86,000 94,600 Oct 85,000 93,500 Nov 84,000 92,400 Dec 83,000 91,300
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
An electronic appliance manufacturer wants to know if there is a relationship between percentage change in deposable personal income which is reported quarterly by the government, and the percentage change in appliances sold by the manufacturer following same years of quarterly data. Brenda Chee and Clarence Paulus lead an analyst team has obtained data for the past 10 quarters. (Hint: Provides your answers in two decimal points) (a) What forecasting model should be used for this data. Why? (b) Develop the forecasting model that you have proposed in (a). (c) Compute the relationship for these data. In your opinion, is the relationship between independent variable strong enough to base a predictive the dependent variable? Explain your answer.
Sales of Volkswagens have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Year Sales 1 460 2 502 3 518 4 575 5 580 Part 2 a) Forecasted sales for year 6 using the trend projection (linear regression) method are enter your response here sales (round your response to one decimal place).
st Info Period 1 2 3 4 5 6 7 8 9 10 K Develop two exponential smoothing forecasts for periods 2 through 11. For the first forecast, use a = 0.3. For the second, use α = 0.7. Assume that your forecast for period 1 was 205. Click the icon to view the time series data. Find the exponential smoothing forecast for each period when a = 0.3 (enter your responses rounded to one decimal place). Demand 222 249 222 228 235 155 161 153 163 152 X Period 1 2 Demand 222 249 Forecast (α = 0.3) 205
It wouldnt let me take a screenshot of the table so I had to copy and paste it in here. My question is: How do you Calculate a moving average forecast using the last 2 and 4 dates of stay. What is the prediction for 7/1/2021 for each technique? Discussion Question 1 - 200 room hotel Forecasts MA 2 Errors MA 4 Errors ES (0.8) Errors ES (0.5) Errors Date Day of Week Rooms Sold MA 2 MA 4 ES (0.8) ES (0.5) AE APE AE APE AE APE AE APE 6/1/2021 Tuesday 123 6/2/2021 Wednesday 109 6/3/2021 Thursday 140 6/4/2021 Friday 199 6/5/2021 Saturday 179 6/6/2021 Sunday 140 6/7/2021 Monday 117 6/8/2021 Tuesday 132 6/9/2021 Wednesday 108 6/10/2021 Thursday 151…

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