(a): Your answer is incorrect.A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recentlysold homes in the space, the following variables are used in a multiple regression model.y sales price (in thousands of dollars)X1= total floor area (in square feet)x₂ = number of bedroomsX3= distance to nearest high school (in miles)The estimated model is as follows.y = 121 +0.083x₁ +23x₂−4x3Answer the questions below for the interpretation of the coefficient of x₁ in this model.(a) Holding the other variables fixed, what is the average change in sales price for each100-square-foot increase in floor area?8.3 dollars(b) Is this change an increase or a decrease?increasedecrease

Here the dependent variable is sales price (y).There are three independent variables.Total floor…

Answered: (a): Your answer is incorrect. A real…

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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The owner of Showtime Movie Theaters, Inc., used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x 1) and newspaper advertising (x 2). The estimated regression equation was Weekly Gross Revenue ($1000s) Televison Advertising ($1000s) Newspaper Advertising ($1000s) 97 6 1.5 91 3 2 95 5 2.5 93 3.5 2.5 96 4 4.3 94 4.5 2.3 95 3.5 4.2 95 4 3.5 ŷ = 82.5 + 2.01 x 1 + 1.26 x 2The computer solution provided SST = 24 and SSR = 22.876. Compute R 2 and R a 2 (to 3 decimals). R 2 R a 2 When television advertising was the only independent variable, R 2 = 0.551 and R a 2 = 0.476. Are the multiple regression analysis results preferable?
The number of megapixels in a digital camera is one of the most important factors in determining picture quality. But, do digital cameras with more megapixels cost more? The following data show the number of megapixels and the price ($) for 10 digital cameras(Consumer Reports, March 2009). Use these data to develop an estimated regression equation that can be used to predict the price of a digital camera given the number of megapixels. Brand and Model Megapixels Price (S) Canon PowerShot SD1100 IS Casio Exilim Card EX-510 8 180 200 230 10 Sony Cyber-shot DSC-T70 Pentax Optio M50 Canon PowerShot G10 120 470 15 8 Canon PowerShot A590 IS Canon PowerShot El 140 180 10 12 Fujifilm FinePix FOOFD Sony Cyber-shot DSC-W170 Canon PowerShot A470 310 10 250 110
The values of advertisement expense (X) and sales (Y) are shown in the table below. Find the regression line. Estimate the value of sales when advertisement expense is 20.
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 owner of Showtime Movie Theatres Inc. would like to predict weekly gross revenue as a function of advertising expenditures. Use 0.05 level of significance.Historical data for a sample of eight weeks follow: Weekly Gross Renvenue Telelvision Newspaper Adveritising ($1000s) Adveritising a) Develop an estimated regression equation to predict weekly gross revenue as a function of advertising expenditures. ($1000s) ($1000s) 96 5.0 1.5 90 20 2.0 b) Explain in simg when 1000s are spent on television and newspaper then the revenue will in 95 4.0 1.5 92 2.5 2.5 c) Predict weekly 95 3.0 3,3 94 3.5 2.3 d) What is the R2 value? 0.9190 94 2.5 4.2 94 3.0 2.5 e) What is the Hypothesis Test? Use the t test to determine the significance of each independent variable. State the t test, p-values, and your conclusion. g) What is the cor Reject Ho SUMMARY OUTPUT pression Statstica Mutiple R 0.958663444 0.9190356 R Square 0.88664984 Adusted R Square Standard Eror 0.642587303 Obervations ANOVA Syaicance…
10) Using advertised prices for used Ford Escorts a linear model for the relationship between a car's age and its price is found. The regression has an R2-87.4%. Write a sentence summarizing what R2 says about this regression. A) The age of the car explains 9.35% of the variation in price. B) The price of the car explains 12.6% of the variation in age. C) The age of the car explains 87.4% of the variation in price. D) The price of the car explains 87.4% of the variation in age. E) The age of the car explains 12.6% of the variation in price. Solve. Round to two decimal places if necessary. 11) At one college, 68% of students have credit cards. A credit card company, looking for new customers, sets up a booth at the college. If they pick students at random, how many students should they expect to talk to before finding one who does not have a credit card? A) 32 B) 3.13 D) 68 C) 0.32 E) 1.47
answer both please and explain well. Anna company sells coffee products to various customers. In recent years, profits have been declining. The CFO of the company investigated the reasons for the profit decline and performed regression analysis for sales and costs. The CFO determined that sales depend on product price, delivery speed, customer services, and marketing expenses. She also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales. Questions 1. Define the coefficient of determination and explain what it means in this scenario. 2. Express the relationship between total costs and variable costs for Anna Company using a regression equation. Explain each element of the equation.
The following data show the brand, price (S), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.522 + 0.335x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 78 B 150 73 95 63 70 58 E 70 38 35 26 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination rt. (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 provided 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 did not provide a good fit as a small proportion of the variability in y…
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. Below is the data that represents the value (in $millions) and the annual revenue (in $millions) for 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 179 460 Boston 310 1000 Chicago White Sox 214 600 Cleveland 178 410 Detroit 217 478 Kansas City 161 354 Los Angeles Angels 226 656 Minnesota 213 510 New York Yankees 439 1850 Oakland 160 321 Seattle 210 585 Tampa Bay 161 323 Texas 233 674 Toronto 188 413 Arizona 186 447 Atlanta 203 508 Chicago Cubs 266 879 Cincinnati 185 424 Colorado 193 464 Houston 196 549 Los Angeles 230 1400 Miami 148 450 Milwaukee 195…
The following data show the brand, price ($), 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…
Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year. Year Number of Patients Robbery Rate per 1,000 Population The simple linear regression equation that shows the best relationship between the number of patients and the robbery rate is (round your responses to three decimal places) where y Number of Patients and x = Robbery Rate. = 1 2 3 4 6 7 36 33 40 41 55 60 58.3 61.1 73.4 75.7 81.1 89.0 101.1 5 40 8 54 94.8 9 58 103.3 10 61 116.2
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