3. Attendance at Los Angeles' newest Disney like attraction, Vacation World has been asfollows:QuarterGuestsQuarterGuests(in Thousands)(in Thousands)2017 Q173Q3124Q2104Q452Q31682019 Q189Q474Q21462018 Q165Q3205Q282Q498a. Graph the data. (Use Excel)b. Compute seasonal indices using all the data. (Solve manually)

A seasonal index measures the values/changes of a particular season of a cycle compared to the…

Answered: Graph the data. (Use Excel) b. Compute…

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_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 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_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?
Do 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.
The following data are for calculator sales in units at an electronics store over the past nine weeks: Week 1 2 3 4 5 Obtain the error measures. (Enter your responses rounded to two decimal places.) CFE MSE Sales Find the coefficient of determination (²). The coefficient of determination r² = 0. (Enter your response rounded to two decimal places.) 44459 46 51 58 Use trend projection with regression to forecast sales for weeks 10-13. What are the error measures (CFE, MSE, 6, MAD, and MAPE) for this forecasting procedure? How about r²? Obtain the trend projection with regression forecast for weeks 10-13. (Enter your responses rounded to two decimal places.) Forecast, Ft Period 10 11 12 13 Week 6 69809 7 Sales 54 63 53 61 MAD U MAPE %
page 4 of 19) - Google Chrome squ.edu.om/mod/quiz/attempt.php?attempt3D1245076&cmid%3663426&page%3D3 earning System (Academic) erations Management || fall20 Quiz stion 4 Mean Absolute Deviation (MAD) is the always the best in assessing a forecast model accuracy yet vered Select one: ked out of O a. True O b. False Hajir lag question 13 Next page 10 19
1. The following table gives the number of pints of type A blood used at Damascus Hospital in the past 6 weeks: Part 2 Week Of Pints Used August 31 350 September 7 372 September 14 412 September 21 381 September 28 366 October 5 378 Part 3 a) The forecasted demand for the week of October 12 using a 3-week moving average LOADING... = _____________pints (round your response to two decimal places).
Obtain estimates of daily relatives for the number of customers at a restaurant for the evening meal, given the following data. a. Use the centered moving average method. (Hint: Use a seven-day moving average.)b. Use the SA method.Day Number Served1 802 753 784 955 1306 1367 408 829 7710 8011 9412 13113 13714 4215 8416 7817 8318 9619 13520 14021 4422 8723 8224 8825 9926 14427 14428 48
Q5) Monthly sales for a six month period are as follows: Month SalesJan 18,000Feb 22,000Mar 16,000Apr 18,000May 20,000June 24,000 Compute the sales forecast for July using the following approaches: (1) Four-month moving average; (2) Weighted three-month moving average using .50 for June, .30 for May and .20 for April;(3) Exponential smoothing with α (smoothing constant) equal to .40, assuming a February forecast of 18,000

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Graph the data. (Use Excel) b. Compute seasonal indices using all the data. (Solve manually) а.