Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand fortheir line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the maindrivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinentinformation was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow:YEAR123456789101112UNIT SALES(THOUSANDS)3966968961,2851,1551,1868961,1189851,334926820PRICE $/UNIT282219206210219209218206220206226236ADVERTISING($000)6008291,1181,4131,2141,2859071,118708907708682a. Perform a regression analysis based on these data using Excel. (Negative values should be indicated by a minus sign. Round youranswers to 4 decimal places.)

Independent variables : Unit price & Advertising Dependent variable : Unit sales i.e., We wish…

Answered: Mark Price, the new productions manager…

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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$339,380 $41,768 A national homebuilder builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price, lot cost, and type of home for closings during one month. Complete parts a through c. E Click the icon to view the house sales data a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "Townhouse", where it is equal to 1 when Type = "Townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sales Price = 108726 + (3.68 ) • Lot Cost + ( - 75063 )• Townhouse (Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places as needed.) b. Determine if an interaction exists between lot cost and type of home and find the best model. Use a = 0.1 as the level of significance. First determine whether an interaction exists. Select the…
Use the given data to find the scatter plot, equation of the regression line, and prediction. 4) Managers rate employees according to job performance and attitude. The results for several randomly selected employees are given below. Performance 59 63 65 69 58 77 76 | 69 70 64 Attitude 72 67 78 82 75 87 92 83 87 78 What function do you use to see the scatterplot on the calculator. Scatterplot from the calculator:
The table shows the lengths and weights of seven muskies captured by the Department of Natural Resources in Catfish Lake in Eagle River, Wisconsin. Use the linear regression feature on a graphing calculator to determine an equation of the line that best fits the data. Round to the hundredths. Musky 1 2 3 4 5 6 7 Length (in.) 26 27 29 33 35 36 38 Weight (lb) 5 8 9 12 14 14 19
The following data show the brand, price ($), 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 = 22.391 + 0.326x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 76 B 150 71 C 95 63 D 70 56 E 70 40 F 35 24 #1) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = #2) Compute the coefficient of determination r2.(Round your answer to three decimal places.) r2 = #2a) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) A) 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. B) The least squares line provided a good fit as a large…
A social scientist would like to analyze the relationship between educational attainment (in years of higher education) and annual salary (in $1,000s). He collects data on 20 individuals. A portion of the data is as follows: Salary 43 49 1 35 Education 7 7 Click here for the Excel Data File a. Find the sample regression equation for the model: Salary Be + B1Education + e. (Round answers to 2 decimal places.) Salary = 37.21 + 6.68 b. Interpret the coefficient for Education. Education As Education increases by 1 year, an individual's annual salary is predicted to increase by $8,590. As Education increases by 1 year, an individual's annual salary is predicted to decrease by $6,680. As Education increases by 1 year, an individual's annual salary is predicted to decrease by $8,590. As Education increases by 1 year, an individual's annual salary is predicted to increase by $6,680. c. What is the predicted salary for an individual who completed 7 years of higher education? (Round coefficient…
One of the biggest changes in higher education in recent years has been the growth of online universities. The Online Education Database is an independent organization whose mission is to build a comprehensive list of the top accredited online colleges. The following table shows the retention rate (%) and the graduation rate (%) for 29 online colleges.a). Use Excel Data Analysis Tool – Regression to get the relationship between the two variables;b). Create a scatter diagram for the two variables and display regression equation and R square on chart, then explain the relationship between the variables;c). Did the estimated regression equation provide a good fit?d). Suppose you were the president of South University. After reviewing the results, would you be able to use the regression result for forecasting. College RR(%) GR(%) Western International University 7 25 South University 51 25 University of Phoenix 4 28 American InterContinental University 29 32 Franklin…
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The accompanying data table gives the value and the annual revenue for 15 major sport teams. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated.
The following data show the brand, price ($), 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 = 21.592 + 0.324x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 76 B 150 69 C 95 61 D 70 56 E 70 38 F 35 24 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) (c) What is the value of the sample correlation coefficient? (Round your answer to three decimal places.)
A. Develop an estimated regression equation that can be used to predict the price of the stock based on the number of shares of company's stocks sold and the volume of exchange. B. Interpret the coefficients of the estimated regression equation that you found in Part a. C. If in a given day, the number of shares of the company that were sold was 94,500 and the volume of exchange on the New York Stock Exchange was 16 million, what would you expect the price of the stock to be?
The following data show the brand, price , and the overall score for six stereo headphones that were tested by Consumer Reports (Consumer Reports website). The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from (lowest) to (highest). The estimated regression equation for these data is , where and .
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through o. Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs OxBillable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) Monthly Overhead Costs and Billable Hours Data Overhead Costs $385,000 Billable Hours 3,000 $425,000 4,000 $445.000 5,000 $497,000 6,000 $570,000 7,000 $590,000 8,000
Susan, a personal trainer, was interested in whether or not there was a linear relationship between the number of visits her clients made to the gym each week and the average amount of time her clients exercised per visit. She took the following data. Client 1 2 3 4 5 6 Number of visits per week 1 3 4 2 3 5 Average time spent exercising per visit (hours) 2 1.5 1 2 2 0.30 Using the best fit line, estimate the average time spent exercising per visit for 4 visits per week. Group of answer choices 1.03 hours 1 hour 10.3 hours 2 hours

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