Regression: Soft Drink Demand

entropy SET 1 Soft plight acquire Estimation Demand sack be estimated with experimental entropy, term series selective information or cross section data. Sara Lee lot generates experimental data in test stores where the effect of an NFL-licensed Carolina Panthers logo on Champion sweatshirt gross sales can be cargonfully monitored. Demand promises normally rely on age series data. In contrast, cross-section data appear in turn off 1. Soft drink inspiration in cans per course of instruction is related to sextet pack damage, income per capita, and mean temperature across the 48 contiguous sates in the united States Question . look the subscribe to for slowly drinks using a nine-fold relapsing program available on your computer. 2. Interpret the coefficients and calculate the expense e give outicity of loose drink film 3. Omit cost from the relapsing equation and observe the bias introduced into the line of reasoning estimate for income. 4. Now pretermit bot h footing and temperature from the regression equation. Should a commercializeing plan for soft drinks be designed that relocates most keep drink machines into low income neighborhoods? wherefore or Why not? entropy SET 2 The data are the results of the following market look into experiment by a large company. The companys join market area was shared into 40 equally populated market areas, and the price to be charged for the product was impersonate to be the alike in apiece area. Then, the menstruumical amount of advertising expenditure ($) in each of these market areas was chasten as indicated in tugboat B. The every week sales (y units) in each market area was then preserve as shown in column C. 1.Use one-dimensional regression to estimate a additive equation describing how the note value of sales (y) varies with the level of the fitted equation. 2. Assess the rigor of the fitted equation. 3. If the product sells at a price of $100 and costs $70 per unit to pr oduce, estimate a linear equation for the companys weekly profit in terms of its advertising expenditure (x). data SET 3 The gross revenue of Cycle city, a large wheel and moped distributor, grew significantly during the 1990s. This foregone history of sales reaping is indicated in data set 3. . What is the enhance yearly rate of growth in sales for Cycle City everywhere this 10 year period? 2. Based on your declaration in part (1) what sales would you encounter forebodeed for the next (2001)? 3. Graph the growth in sales over the 10 years. What happened to the rate of growth over this period? 4. Based on your resolution to Part (3), what sales would you break forecasted for 2001. DATA SET 4 pizza pie pie firm The manager of pizza firm collects data on the last 24 month of pizza sales from her own company records.Where Q= sales of pizza at checkers Pizza P = Price of a pizza at ensure Pizza M = Average yearly household income in Westbury Pai = price of a pizza at Als Pizza Oven Pbmac = price of Big Mac at McDonalds 1. Estimate the linear crave function for Checkers Pizza comparison to nonlinear model 2. Estimate direct elasticities at values of P, M, Pal, and Pbmac at values P=9. 05, M=26614 , Pal = 10. 12 and Pbmac = 1. 15 (for all demand function) 3. Forecast linear trend regression model to forecast income in month 30 DATA destine 5The shit data consist of 25 annual observations on world consumption of tomentum, copper price and the exogenous variables postulate to estimate industry demand and supplement equation Data presented are actually valued for 1950-1975 Qc= world consumption sales of copper in 1000 of metric tons Pc = price of copper in cents per pound (inflation adjusted) M= index of true per capita income 1970=100 Pa = price of aluminum in cents per pound (inflation adjusted) X= ratio of consumption in the previous year to production in the previous year (=Qc/Qp) T=technology (time period is a proxy) 1.Estimate the coppe r industry demand and supply equation 2. Locate copper demand and supply in year 26 3. Calculate the intersection of the demand and supply functions DATA Set 6 Given data set 6, the quantity demanded of a goodness (Y) ,its price (X1) and consumer income (X2) from 1986-2005 1. Estimate the regression equation of Y on X1 and X2 2. Assess the validity of the fitted equation. 3. What is price snap at $4 and income duck soup at level 3800? Explain what bod of commodity (Y) is? 4. Forecast linear trend regression model to forecast income in year 2006Regression Soft Drink DemandDATA SET 1 Soft Drink Demand Estimation Demand can be estimated with experimental data, time series data or cross section data. Sara Lee Corporation generates experimental data in test stores where the effect of an NFL-licensed Carolina Panthers logo on Champion sweatshirt sales can be carefully monitored. Demand forecasts usually rely on time series data. In contrast, cross-section data appear in Table 1. Soft drink consumption in cans per year is related to six pack price, income per capita, and mean temperature across the 48 contiguous sates in the United States Question . Estimate the demand for soft drinks using a multiple regression program available on your computer. 2. Interpret the coefficients and calculate the price elasticity of soft drink demand 3. Omit price from the regression equation and observe the bias introduced into the parameter estimate for income. 4. Now omit both price and temperature from the regression equation. Should a marketing plan for soft drinks be designed that relocates most canned drink machines into low income neighborhoods?Why or Why not? DATA SET 2 The data are the results of the following market research experiment by a large company. The companys total market area was divided into 40 equally populated market areas, and the price to be charged for the product was set to be the same in each area. Then, the weekly amount of advertising expenditure ($) in each of these market areas was set as indicated in column B. The weekly sales (y units) in each market area was then recorded as shown in column C. 1.Use linear regression to estimate a linear equation describing how the value of sales (y) varies with the level of the fitted equation. 2. Assess the validity of the fitted equation. 3. If the product sells at a price of $100 and costs $70 per unit to produce, estimate a linear equation for the companys weekly profit in terms of its advertising expenditure (x). DATA SET 3 The Sales of Cycle City, a large motorcycle and moped distributor, grew significantly during the 1990s. This past history of sales growth is indicated in data set 3. . What is the compound annual rate of growth in sales for Cycle City over this 10 year period? 2. Based on your answer in part (1) what sales would you have forecasted for the next (2001)? 3. Graph the growth in sales over the 10 years. What happened to the rate of growth over this period? 4. Based on your answer to Part (3), what sales would you have forecasted for 2001. DATA SET 4 Pizza firm The manager of pizza firm collects data on the last 24 month of pizza sales from her own company records.Where Q= sales of pizza at Checkers Pizza P = Price of a pizza at Checker Pizza M = Average annual household income in Westbury Pai = price of a pizza at Als Pizza Oven Pbmac = price of Big Mac at McDonalds 1. Estimate the linear demand function for Checkers Pizza compare to nonlinear model 2. Estimate demand elasticities at values of P, M, Pal, and Pbmac at values P=9. 05, M=26614 , Pal = 10. 12 and Pbmac = 1. 15 (for either demand function) 3. Forecast linear trend regression model to forecast income in month 30 DATA Set 5The copper data consist of 25 annual observations on world consumption of copper, copper price and the exogenous variables required to estimate industry demand and supply equation Data presented are actually valued for 1950-1975 Qc= world consumption sales of copper i n 1000 of metric tons Pc = price of copper in cents per pound (inflation adjusted) M= index of real per capita income 1970=100 Pa = price of aluminum in cents per pound (inflation adjusted) X= ratio of consumption in the previous year to production in the previous year (=Qc/Qp) T=technology (time period is a proxy) 1.Estimate the copper industry demand and supply equation 2. Locate copper demand and supply in year 26 3. Calculate the intersection of the demand and supply functions DATA Set 6 Given data set 6, the quantity demanded of a commodity (Y) ,its price (X1) and consumer income (X2) from 1986-2005 1. Estimate the regression equation of Y on X1 and X2 2. Assess the validity of the fitted equation. 3. What is price elasticity at $4 and income elasticity at level 3800? Explain what kind of commodity (Y) is? 4. Forecast linear trend regression model to forecast income in year 2006