A student working on a summer internship in the economic research office of a large corporation, studied the relation between sales of a product (in million dollars) and population in the firm's 50 marketing districts. The computer output appears below: T VALUE PARAMETER STD ERROR PR >T 7.411 0.002 INTERCEPT 1.9802 2.1756 POPULATION 0.7550 0.2192 0.1043 0.018 The fitted regression line is: OY= 0.755 +7.411X OY= 7.411+0.755X O Y = 2.1756 + 0.1043X OY=7.411+ 7.55X
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4In order to determine the relationship between the number of units sold of a company's product (y) in 11 cities with their major competitor's price (x1) in dollars, and the number of stores (x2) the competitor has in each city, the following data were collected. Units sold CompetitorPrice CompetitorStores 510 35 5 680 45 0 590 40 2 650 45 1 600 40 2 600 41 1 540 40 5 575 46 6 600 41 2 550 35 3 560 39 3 Generate a linear multiple regression output for the data.a) Report the regression coefficients accurate to 3 decimal places: ˆy^= ---- +------ x1 + -------x2 b) Report the coefficient of determination accurate to 3 decimal places: R2=In order to determine the relationship between the number of units sold of a company's product (yy) in 9 cities with their major competitor's price (x1x1) in dollars, and the number of stores (x2x2) the competitor has in each city, the following data were collected. Units sold CompetitorPrice CompetitorStores 510 35 5 600 44 3 600 41 2 600 40 2 650 45 1 590 40 2 600 41 1 540 40 5 590 40 2 Generate a linear multiple regression output for the data.a) Report the regression coefficients accurate to 3 decimal places:ˆyy^= + x1 + x2b) Report the coefficient of determination accurate to 3 decimal places:R2=
- Which of the following does not need to be computed to determine a simple regression line? SSx SP "Y-hat" SSySuppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers: Area – area of the kitchen in square feet Height – ceiling height in the kitchen (from floor to ceiling) in inches Cabinets – number of cabinets in the kitchen Suppose that a multiple linear regression model was fit to the data and that the following output resulted: Coefficients: (Intercept)HeightCabinets Estimate-57.98771.2760.3393 Std. Error8.63820.26430.1302 t value -6.7134.8282.607 Pr(>|t|)2.75e-074.44e-050.0145 What is the predicted area of a kitchen with a height of 96 inches and 10 cabinets? Report your answer to 1 decimal place. square feetSuppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers: Area – area of the kitchen in square feet Height – ceiling height in the kitchen (from floor to ceiling) in inches Cabinets – number of cabinets in the kitchen Suppose that a multiple linear regression model was fit to the data and that the following output resulted: Coefficients: (Intercept)HeightCabinets Estimate-57.98771.2760.3393 Std. Error8.63820.26430.1302 t value -6.7134.8282.607 Pr(>|t|)2.75e-074.44e-050.0145 10 Question 10 This is not a form; we suggest that you use the browse mode and read all parts of the question carefully. Which of the following is the correct interpretation of the coefficient for Cabinets? For a kitchen with a given ceiling height, the average number of cabinets…
- Suppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers: Area – area of the kitchen in square feet Height – ceiling height in the kitchen (from floor to ceiling) in inches Cabinets – number of cabinets in the kitchen Suppose that a multiple linear regression model was fit to the data and that the following output resulted: Coefficients: (Intercept)HeightCabinets Estimate-57.98771.2760.3393 Std. Error8.63820.26430.1302 t value -6.7134.8282.607 Pr(>|t|)2.75e-074.44e-050.0145 Why is the interpretation of the constant term (i.e. "intercept") not meaningful for this example? The predicted area will be negative when the number of cabinets is zero and the height of the kitchen is also zero. But we cannot have a negative area, nor a kitchen ceiling height of 0 inches.…A U.S. state's Bureau of Economic Geology published a study on the economic impact of using carbon dioxide enhanced oil recovery (EOR) technology to extract additional oil from fields that have reached the end of their conventional economic life. The following table gives the approximate number of jobs for the citizens that would be created at various levels of recovery. Percent Recovery (%) 20 40 80 100 Jobs Created (Millions) 6 9 12 18 Find the regression line. j(r) = Use the regression line to estimate the number of jobs that would be created at a recovery level of 60%. _____ million jobsInterpret the estimated regression coefficient corresponding to the Z variable. Data Salary Education Experience Sex 29.7985 15 3 1 21.8219 4 0 0 22.8978 4 0 0 22.0917 1 1 0 21.8993 5 0 0 22.4829 3 1 1 28.0772 15 0 0 y=salary 23.6292 6 1 1 x1=education level in schooling years 32.3595 0 15 1 x2=experience level in employment level 21.794 1 0 0 d=sex (1 for male,0 for female) 19.8762 3 0 0 Ln(Y) = alpha +beta1X1 +Beta2X2+ Beta3D +Beta4Z +e 21.0253 3 0 0 where z =X2D 24.6323 0 5 1 19.0247 0 0 0 18.8857 0 0 0 21.8552 1 0 0 24.2675 6 1 0 18.7931 0 0 0 18.9276 0 0 0 23.4441 5 1 1 20.8047 2 0 0 18.26 0 0 0 20.6726 0 2 1 21.7815 3 0 0…
- In a certain type of metal test specimen, the normal stress on a specimen is known tobe functionally related to the shear resistance. The following is a set of codedexperimental data on the two variables:Normal stress (X) 26.8 25.4 28.9 23.6 27.7 23.9 24.7Shear resistance (Y) 26.5 27.3 24.2 27.1 23.6 25.9 26.3i) Estimate the linear regression line and interpret regressioncoefficient.ii) Comment about of goodness of fit of the estimated regression line.Suppose a commercial developer in Vereeniging consider to purchase a group of small office buildings in an established business district. He uses multiple linear regression analysis, which was based on a sample of 35 office buildings, to estimate the value of an office building in a given area based on the following variables. Y = Assessed value of the office building (in Rand) X1= Floor space in square meters X2= Number of offices X3= Age of the office building in years Answer the questions that follow by typing only the letter of the correct option (A, B, C, D or E) in the answer spaces provided. Variablesy: Valuex1: Floor Spacex2: Officesx3: Age Model Fitting StatisticsR^2 = 0.9752Adj R^2: ? Regression Coefficients Beta Parameter Standard b Parameter Standard Estimates Error of Beta Estimates Error of b t Statistic Prob > |t|Intcpt…Calculate the R2of the following multivariate sample regression functions and interpret theanswers.3.1 Investment-hat = β1-hat + β2-hat*Interest rate + β3-hat*Exchange rateESS = 900RSS = 1003.2 Investment-hat = β1 + β2-hat*Interest rate + β3-hat*number of 311 studentsESS = 400RSS = 6003.3 Salary-hat = β1 + β2-hat*Frequency of blinking eyes + β3-hat*Colour of hairRSS = 950TSS = 1000