Better if you use excel in the computations. Thank you. FORECASTING - Linear RegressionGeneral instruction: Solve the following problem as directed. Show your solutions. All computedvalues should be rounded off to three decimal places. Show your answers clearly.Lizzie wants to open a fashion boutique business. Before selecting a location, shewould like to be able to predict the profit in pesos that the store maybe expected to earn perhundred square feet of selling space. She gathers the following data:Store SizeProfit1. Identify the independent anddependent variabler2. Compute for the correlationcoefficient (.3. Based on 2, explain therelationship between the twovariables. Discuss the implicationof this relationship ").4. Solve for the y-intercept and theslope i-5. Form the equation of theregression line that describes therelationship between the twovariables(100 sq. ft.) (R 10,000)35202215271716281612740226. Predict the profit of the fashionboutique business when the storemeasures 3800 square feet.Explain the result/3223"Er-(E*)

The excel output for the above mentioned problem is as follows,

FORECASTING - Linear Regression General instruction: Solve the following problem as directed. Show your solutions. All computed values should be rounded off to three decimal places. Show your answers clearly. Lizzie wants to open a fashion boutique business. Before selecting a location, she would like to be able to predict the profit in pesos that the store maybe expected to earn per hundred square feet of selling space. She gathers the following data: Profit (R 10,000) Store Size 1. Identify the independent and dependent variabler 2. Compute for the correlation coefficient (-. 3. Based on 2, explain the relationship between the two variables. Discuss the implication of this relationship " ). 4. Solve for the y-intercept and the slope i. 5. Form the equation of the regression line that describes the relationship between the two variables" (100 sq. ft.) 35 20 22 15 27 17 16 28 16 12 7 40 22 6. Predict the profit of the fashion boutique business when the store measures 3800 square feet. Explain the resuit/ 32 23

FORECASTING - Linear Regression General instruction: Solve the following problem as directed. Show your solutions. All computed values should be rounded off to three decimal places. Show your answers clearly. Lizzie wants to open a fashion boutique business. Before selecting a location, she would like to be able to predict the profit in pesos that the store maybe expected to earn per hundred square feet of selling space. She gathers the following data: Profit (R 10,000) Store Size 1. Identify the independent and dependent variabler 2. Compute for the correlation coefficient (-. 3. Based on 2, explain the relationship between the two variables. Discuss the implication of this relationship " ). 4. Solve for the y-intercept and the slope i. 5. Form the equation of the regression line that describes the relationship between the two variables" (100 sq. ft.) 35 20 22 15 27 17 16 28 16 12 7 40 22 6. Predict the profit of the fashion boutique business when the store measures 3800 square feet. Explain the resuit/ 32 23

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.4: Multiple Regression Models

Problem 17P: The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He...

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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 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_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.
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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?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
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Movieflix, an online movie streaming service that offers a wide variety of award-winning TV shows, movies, animes, and documentaries, would like to determine the mathematical trend of memberships in order to project future needs. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021Membership (000s) 17 16 16 21 20 20 23 25 24 1. Provide an example of qualitative forecasting and explain the shortcomings.
The number of patients coming to the Healthy Start Maternity clinic has been increasing steadilyover the past eight months. You are provided with some historical data as follows: Month Clinic attendance (in thousands)1 3.42 3.93 4.54 5.05 5.86 5.97 6.58 6.7 a. Identify and give a brief explanation of the:i. Dependent variableii. Independent variable b. Use simple linear regression to forecast annual demand for months 9 and 10 by using thetabular method to:i. Derive the values for the intercept and slope. ii. Derive the linear regression equation. iii. Plot the linear regression line. iv. Develop a forecast of the clinic attendance for months 9 and 10.