Using simple linear regression and given that the price per cup is $1.75, the forecasted demand for mocha latte coffees will be (enter your response rounded to one decimal place). cups
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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.Management of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.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 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?Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values: Price Number Sold $2.50 760 $3.40 515 $2.10 980 $4.20 245 $3.00 325 $4.00 475 Using simple linear regression and given that the price per cup is $1.75, the forecasted demand for mocha latte coffees will be _____?_____ cups (enter your response rounded to one decimal place).Global 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.)
- 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.The number of patients coming to the Healthy Start Maternity clinic has been increasing steadily over 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 6.7a. 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 the tabular 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.The following equation summarizes the trend portion of quarterly sales of condominiums over a long cycle. Sales also exhibit seasonal variations. Using the information given, prepare a forecast of sales for each quarter of next year (not this year), and the first quarter of the year following that.Ft = 40 – 6.5t + 2t2whereFt = UnitSales t = 0 at, the first quarter of last yearQuarter Relative1 1.12 1.03 .64 1.3
