BipinSubject; Suppose the Sherwin-Williams Company is interested in developing a simple regression model with paint sales (Y) as the dependent variable andselling price (P) as the independent variable.Complete the following worksheet and then use it to determine the estimated regression line.Sales Regioni1NM + 55 00 a23678910TotalRegression ParametersSlope (B)Intercept (a)15.166O 16.61418.6323.299Selling Price($/Gallon)Xi1513.516.514.5171613181215.52.6851512.942Sales(x 1000 Gal)What is the standard error of the estimate (se)?NoYesYi160220140190-14.20387.43120160190150210190In words, for a dollar increase in the selling price, the expected sales will increase by 14,2001,730EstimationsXi Yi2,4002,9702,3102,7552,0402,5602,4702,7002,5202,945What is the estimate of the standard deviation of the estimated slope (st)?25,670X₂ 2225182.25272.25210.25289256169324144240.25Yi25,60048,40019,60036,10014,40025,60036,10022,50044,10036,1002,312 308,500Can you reject the hypothesis (at the 0.05 level of significance) that there is no relationship (i.e., B= 0) between the variables? (Hint:to.025,8 = 2.306)gallons in a given sales region.

The provided data set is given as:Sales region; Selling price ($/gallon); Sales ();…

Answered: Suppose the Sherwin-Williams Company is…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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Suppose the Sherwin-Williams Company is interested in developing a simple regression model with paint sales (Y) as the dependent variable and selling price (P) as the independent variable. Complete the following worksheet and then use it to determine the estimated regression line. Sales Region Selling Price Sales ($/Gallon) (x 1000 Gal) ii xixi yiyi xixiyiyi xi2xi2 yi2yi2 1 15 160 2,400 225 25,600 2 13.5 220 2,970 182.25 48,400 3 16.5 140 2,310 272.25 19,600 4 14.5 190 2,755 210.25 36,100 5 17 120 2,040 289 14,400 6 16 160 2,560 256 25,600 7 13 210 2,730 169 44,100 8 18 150 2,700 324 22,500 9 12 220 2,640 144 48,400 10 15.5 190 2,945 240.25 36,100 Total 151 1,760 26,050 2,312 320,800 Regression Parameters Estimations Slope (ββ) -16.49 Intercept (αα) 424.98 In words, for a dollar increase in the selling price, the expected sales will increase by 2,640 gallons in a given sales region.…
Suppose the Sherwin-Williams Company is interested in developing a simple regression model with paint sales (Y) as the dependent variable and selling price (P) as the independent variable. Complete the following worksheet and then use it to determine the estimated regression line. Sales Region Selling Price Sales ($/Gallon) (x 1000 Gal) ii xixi yiyi xixiyiyi xi2xi2 yi2yi2 1 15 160 2,400 225 25,600 2 13.5 220 2,970 182.25 48,400 3 16.5 140 2,310 272.25 19,600 4 14.5 190 2,755 210.25 36,100 5 17 120 2,040 289 14,400 6 16 160 2,560 256 25,600 7 13 210 2,730 169 44,100 8 18 150 2,700 324 22,500 9 12 210 2,520 144 44,100 10 15.5 190 2,945 240.25 36,100 Total 151 1,750 2,312 What is the estimate of the standard deviation of the estimated slope (sbsb)? 2.627 3.173 2.877 Can you reject the hypothesis (at the 0.05 level of significance) that there is no relationship (i.e., β=0β=0) between the…
The owner of Showtime Movie Theaters, Inc., would like to predictweekly gross revenue as a function of advertising expenditures.Historical data for a sample of eight weeks follow. Weekly GrossRevenue($1000s) Television Advertising($1000s) Newspaper Advertising($1000s) 9690959295949494 5.02.04.02.53.03.52.53.0 1.52.01.52.53.32.34.22 .5 a. Develop an estimated regression equation with the amount oftelevision advertising as the independent variable.b. Develop an estimated regression equation with both televisionadvertising and newspaper advertising as the independent variables.c. Is the estimated regression equation coefficient for televisionadvertising expenditures the same in part (a) and in part (b)?Interpret the coefficient in each case.d. Predict weekly gross revenue for a week when $3500 is spent ontelevision advertising and $1800 is spent on newspaper advertising.
A mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs. Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes. Variablesy: Warehouse Distribution Costx1: Salesx2: Number of Orders Model Fitting StatisticsR2 = 0.8504Adj R2: ? Regression Coefficients Beta Parameter Standard b Parameter Standard Estimates…
Jensen Tire & Auto is deciding whether to purchase a maintenance contract for its newcomputer wheel alignment and balancing machine. Managers feel that maintenance expenseshould be related to usage, and they collected the following information on weeklyusage (hours) and annual maintenance expense (in hundreds of dollars). a. Develop a scatter chart with weekly usage hours as the independent variable. Whatdoes the scatter chart indicate about the relationship between weekly usage and annualmaintenance expense?b. Use the data to develop an estimated regression equation that could be used to predictthe annual maintenance expense for a given number of hours of weekly usage. Whatis the estimated regression model? c. Test whether each of the regression parameters b0 and b1 is equal to zero at a 0.05level of significance. What are the correct interpretations of the estimated regressionparameters? Are these interpretations reasonable?d. How much of the variation in the sample values of…
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 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 feet
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 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 ski resort asked a random sample of guests to rate their satisfaction on various attributes of their visit on a scale of 1–5 with 1 = very unsatisfied and 5 = very satisfied. The estimated regression model was Y = overall satisfaction score, X1 = lift line wait, X2 = amount of ski trail grooming, X3 = safety patrol visibility, and X4 = friendliness of guest services. Predictor Coefficient Intercept 2.9018 LiftWait 0.1642 AmountGroomed 0.2343 SkiPatrolVisibility 0.0602 FriendlinessHosts −0.1193 (a) Write the fitted regression equation. (Round your answers to 4 decimal places. Negative values should be indicated by a minus sign.) yˆy^ = + * LiftWait + * AmountGroomed + * SkiPatrolVisibility + * FriendlinessHosts (b) Interpret each coefficient. Overall satisfaction (Click to select) increases decreases remains same with an increase in satisfaction for each individual predictor except for friendliness of hosts.(c) Would the intercept seem to…
A ski resort asked a random sample of guests to rate their satisfaction on various attributes of their visit on a scale of 1–5 with 1 = very unsatisfied and 5 = very satisfied. The estimated regression model was Y = overall satisfaction score, X1 = lift line wait, X2 = amount of ski trail grooming, X3 = safety patrol visibility, and X4 = friendliness of guest services. Predictor Coefficient Intercept 2.7115 LiftWait 0.1474 AmountGroomed 0.2517 SkiPatrolVisibility 0.0656 FriendlinessHosts −0.1172 (a) Write the fitted regression equation. (Round your answers to 4 decimal places. Negative values should be indicated by a minus sign.) yˆy^ = ?? + ?? * LiftWait + ?? * AmountGroomed + ?? * SkiPatrolVisibility + ?? * FriendlinessHosts (b) Interpret each coefficient. Overall satisfaction increases Correctwith an increase in satisfaction for each individual predictor except for friendliness of hosts.(c) Would the intercept seem to have meaning in this…
The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenueas a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly GrossRevenue($1000s) Television Advertising($1000s) Newspaper Advertising($1000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 a. Develop an estimated regression equation with the amount of televisionadvertising as the independent variable.b. Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. c. Is the estimated regression equation coefficient for television advertisingexpenditures the same in part (a) and in part (b)? Interpret the coefficient in each case. d. Predict weekly gross revenue for a week when $3500 is spent on television advertising and $1800 is spent on newspaper advertising.
The owner of Showtime Movie Theaters, Inc. would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly GrossRevenue($1000s) TelevisonAdvertising($1000s) NewspaperAdvertising($1000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 Part A: Develop an estimated regression equation with the amount of television advertising as the independent variable. Part B: Develop an estimated regression equation with both television advertising and news paper advertising as independent variables. Part C: Is the estimated regression rquation coefficient for television advertising expenditures the same in part (a) and in part (b) ? Interpret the coefficient in each case. Part D : Predict Weekly gross revenue for a week $3500 is spent on television advertising and $1800 is spent on newspaper advertising? Please hurry

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