---### Multiple Regression Analysis for Predicting Paint Sales#### Model Overview:Suppose the Sherwin-Williams Company has developed the following multiple regression model, where paint sales $ Y $ (in 1,000 gallons) is the dependent variable and promotional expenditures $ A $ (x $1,000) and selling price $ P $ (dollars per gallon) are the independent variables:\[ Y = \alpha + \beta_A A + \beta_P P + \epsilon \]#### Model Estimates:The estimates for the parameters of the model produce the following results:- Intercept $\alpha = 344.585$- Coefficient for $ A $: $ \beta_A = 0.106$- Coefficient for $ P $: $ \beta_P = -13.397$Standard errors:- $ s_{\alpha} = 0.164 $- $ s_{\beta_A} = 4.487 $- $ s_{\beta_P} = 10.372 $Additional statistics:- $ R^2 = 0.813 $- $ F \text{-statistic} = 10.372 $**Note**: The sample consists of 10 observations.---### Questions and Analysis:#### 1. Impact of Promotional Expenditures and Selling Price:According to the estimated model, holding all else constant:- A $1,000 increase in promotional expenditures $ A $ increases sales by approximately [___] gallons.- A $1 increase in the selling price $ P $ decreases sales by approximately [___] gallons.#### 2. Statistical Significance of Independent Variables:Which of the independent variables, if any, appears to be statistically significant (at the 0.05 level) in explaining paint sales? Check all that apply.- [ ] Selling price (P)- [ ] Promotional expenditures (A)#### 3. Proportion of Total Variation in Sales:What proportion of the total variation in sales is explained by the regression equation?- [ ] 0.164- [ ] 0.106- [ ] 0.813#### 4. Interpreting the F-Value:The given F-value shows that you [___] reject the null hypothesis that neither one of the independent variables explains a significant (at the 0.05 level) proportion of the variation in income.#### 5.

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Answered: Suppose the Sherwin-Williams Company…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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 i 1 2 3 4 5 6 7 8 9 10 Total Selling Price ($/Gallon) I; 15 13.5 16.5 14.5 17 16 13 18 12 15.5 151 Regression Parameters Slope (B) Intercept (a) Sales (x 1000 Gal) Yi 160 220 140 190 120 160 200 150 210 190 1,740 Estimations TiYi 2,400 2,970 2,310 2,755 2,040 2,560 2,600 2,700 2,520 2,945 25,800 ▼ x ₂² 225 182.25 272.25 210.25 289 256 169 324 144 240.25 2,312 In words, for a dollar increase in the selling price, the expected sales will Yi² 25,600 48,400 19,600 36,100 14,400 25,600 40,000 22,500 44,100 36,100 286,800 ▼ by gallons in a given sales region.
The sweetness, y, of the fruit is supposed to be related to the average daily sunshine hours, x. The following data shows the sweetness of the same type of fruit at different locations (sunshine hours). Fit the data to a simple linear regression model. x: 5, 6, 7, 6, 6, 8, 7, 5. y: 9, 10, 10, 11, 12, 13, 12, 8. . Calculate SSE, SST, ² and R². And interpret R².
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 21.656 + 0.333x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 76 B 150 73 95 61 70 54 E 70 42 35 24 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination r. (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line provided a good fit as a small proportion of the variability in y…
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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 B 9 10 11 12 UNIT SALES (THOUSANDS) 398 698 898 y bar 1,304 1,163 1,195 898 1,200 982 1,235 875 811 PRICE $ PER UNIT 283 209 213 220 209 200 220 209 230 213 216 243 ADVERTISING ($000) 621 821 1,200 1,405 1,210 1,304 875 1,200 691 875 691 691 a. Perform a regression analysis based on these data using Excel. Note: Negative values should be indicated by a minus sign. Round your answers to 4 decimal places. Price advertising
A study was undertaken to compare the average high-density lipoprotein (HDL) levels of normal and obese adults in a certain community. HDL levels of a random sample of adults from this community were measured (in milligrams per deciliter or mg/dL) and presented below. Assume that the HDL levels of both the normal and obese adults follow the normal distribution with unknown but equal population variances. Is there a significant difference between the average HDL levels of normal and obese adults? Use a 5% level of significance.
The police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
The manager of a car dealership wants to predict the relationship between number of sales persons and the number of cars sold. He collects some data to develop a regression model, which is given below Week Number of cars sold Number of salespersons 1 80 6 2 90 8 3 48 4 4 55 5 5 60 7 6 75 9 7 100 11 8 70 6 9 65 7 10 98 10 (i) Run a regression analysis which explains the number of cars sold in terms of number of salespersons, and estimate the regression line (ii) Interpret the regression coefficient, and determine the significance of the regression coefficient (iii) Interpret the overall model fit…
For the provided data, develop a regression model for overall satisfaction as a function of years of service and department that has the largest R. Note that the categorical variable department has multiple levels and will require the use of multiple dummy variables. Which department, if any, has the highest impact on roduction satisfaction? Production Production E Click the icon to view the job satisfaction data. Determine the regression model for overall satisfaction as a function of years of service and department that has the largest R. Let "Administrative" be the baseline department, let X, represent Maintenance, let X, represent Management, let X, represent Production, let X, represent Quality Control, and let Xg represent Shipping / Receiving, coding each department variable with a 1 if the person is in that department and 0 otherwise. In addition, let Xe represent Years. Enter the terms of the equation so that the X-values are in ascending numerical order by base. Select the…
The sweetness, y, of the fruit is supposed to be related to the average daily sunshine hours, x. The following data shows the sweetness of the same type of fruit at different locations (sunshine hours). Fit the data to a simple linear regression model. x: 5, 6, 7, 6, 6, 8, 7, 5. y: 9, 10, 10, 11, 12, 13, 12, 8. Predict the true mean sweetness for average daily hours of 8 hours, and calculate the residual for average daily sunshine hours of 8 hours.
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.525 + 0.325x, where x = price ($) and y overall score. %3D Brand Price ($) Score 180 78 B. 150 71 C 95 59 70 54 E 70 40 35 28 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST SSR %3D SSE = (b) Compute the coefficient of determination . (Round your answer to three decimal places.) 1 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y…

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