Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month, Gershon has collected the data shown in the table below. Using Linear Regression, if Maroon 5 appeared in 15 TV appearances, what would the expected demand for Guitars? Maroon 5 TV Appearances 3 8 6 7 7 Demand for Guitars 2 688476 5 11

Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month, Gershon has collected the data shown in the table below. Using Linear Regression, if Maroon 5 appeared in 15 TV appearances, what would the expected demand for Guitars? Maroon 5 TV Appearances 3 8 6 7 7 Demand for Guitars 2 688476 5 11

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.3: Simple Regression Models

Problem 6P: Do the sales prices of houses in a given community vary systematically with their sizes (as measured...

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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 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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Year Season Sales 2018 Winter 40 2018 Spring 29 2018 Summer 31 2018 Fall 40 2019 Winter 102 2019 Spring 87 2019 Summer 96 2019 Fall 132 2020 Winter 105 2020 Spring 93 2020 Summer 105 2020 Fall 117 2021 Winter 141 2021 Spring 39 2021 Summer 114 2021 Fall 123 What is the slope of the trend equation obtained by linear regression? Round to two decimal digits. What is the intercept of the trend equation obtained by linear regression? Round to two decimal digits. What is the seasonal index for Spring? Round to two decimal digits. The quarter number for Winter of 2018 is 1. What is the quarter number for Spring of 2025? What is the trend based forecast for Spring of 2025. Round to a whole number. What is the seasonally adjusted trend based forecast for Spring of 2025? Please do not use excel to find the slope and intercept, thank you so much!
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1- A pharmacist has been monitoring sales of certain over-the-counter pain reliever. Daily sales during the last 15 days were as follows: Day 1 2 3 4 7 8 9 Number sold 36 38 42 44 48 49 50 49 52 Day 10 11 12 13 14 15 Number sold 48 52 55 54 56 57 a: By using linear regression method predict the future sales for the next 5 days (please do all calculations in excel and send the excel file with answers included as your response) b: calculate the MSE for the prediction of these 15 days. (again by using Excel) 1- Explain how QFD can be employed for designing a new restaurant in your region in Iraq? (at least 100 words) 2- A producer of inkjet printers is planning to add a new production line of printers, and you have been asked to balance the process, given the following task times and precedence relationships. Assume the cycle time is to be minimum possible (based on the bottleneck). Immediate Length (minutes) Task (Predecessor) a 0.2 b 0.4 a 0.3 d 1.3 b, c e 0.1 f 0.8 e g 0.3 d, f 1.2 g a…
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Please answer parts i, ii and iii of the below question. The table below shows the sales figures for a brand of shoe over the last 12 months. Months Sales January 69 February 75 March 86 April 92 May 95 June 100 July 108 August 115 September 125 October 131 November 140 December 150 Using the following, forecast the sales for the months up to January the following year:- i) A simple three month moving average. ii) A three period weighted moving average using weights of 1, 2 and 3. Assign the highest weight to the most recent data. iii) Exponential Smoothing when α= .6 and the forecast for March is 350.