Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of company speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow: YEAR 1 2 3 4 5 6 7 UNIT SALES (THOUSANDS) 397 697 897 1,280 1,154 1,187 897 PRICE $/UNIT 281 220 207 211 220 208 217 ADVERTISING ($000) 575 828 1,117 1,412 1,215 1,280 906
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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?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?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.Refer to the following null hypothesis formulated by a restaurant manager who wanted to investigate the factors that encourage the customers to return to their restaurant in future. Identify which data analysis method is the most appropriate for the null hypothesis below. * "There is no relationship between Return in Future and Service Quality, Food Quality and Food Presentation". t-test one-way ANOVA correlation O multiple regressionGlobal Bikes Inc. (a bicylce manufacturing firm) collected the following data during a study of consumer buying patterns. Observation 1 2 3 4 5 6 7 y= X y 15 70 28 79 38 89 35 86 49 97 42 97 30 82 y = Observation 0.721 + 1890123 a. Create a linear regression line (equation) for the data.(Round your Intermediate calculations and final answers to 3 decimal places.) X 14 16 14 24 21 29 63.229 X y 82 70 68 84 83 91 b. Use the equation determined in part b to predict the expected value of y for x = 49. (Round your Intermediate calculations and final answers to 3 decimal places.)
- 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…Develop Linear Regression Model. Forecast sales for 8, 14 and 17 years of education. Sales(1000s) Years of Education 24 10 18 9 20 11 22 6 26 13 23 16 21 18A pharmacist has been monitoring sales of a certain over-the-counter pain reliever. Daily sales duringthe last 15 days wereDay: 1 2 3 4 5 6 7 8 9Number sold: 36 38 42 44 48 49 50 49 52Day: 10 11 12 13 14 15Number sold: 48 52 55 54 56 57a. Using linear trend equation method, predict future sales from the given data.b. If you learn that on some days the store ran out of the specific pain reliever, would that knowledge cause you any concern? Explain.
- . What are the advantages and disadvantages of Regression Model, Econometric Model, Driving Indicator Models?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.Consider the monthly sales data of a company for last year as well as first six month data for current year. Use a three- quarter weighted moving average, Forecast the sales of company for 3rd Quarter of current year. Use Weights of 4/7, 2/7 and 1/7, giving more weight to more recent data. Note, the 1ist quarter is Jan, Feb and March , 2nd quarter is Apr, May, June, 3rd quarter is July, Aug and Sept, and 4th quarter is Oct, Nov and Dec. Month Jan Feb Mar April May June July Aug Sept Oct Nov DecLast Year 100 125 135 175 185 200 150 140 130 200 225 250Current Year 125 135 135 190 200 190