We have data from 209 publicly traded companies (circa 2010) indicating sales and compensationinformation at the firm-level. We are interested in predicting a company's sales based on the CEO'ssalary. The variable sales; represents firm i's annual sales in millions of dollars. The variablesalary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares toestimate the linear regressionsales; = a + ßsalary; + eiand get the following regression results:regress sales salary.SourceModelResidualTotalsalessalary_consSS3379204052.3180e+102.3518e+10df1207208Coef. Std. Err..9287785 .53465745733.917 1002.477MS337920405111980203113066454tNumber of obsF(1, 207)Prob> FR-squaredAdj R-squaredRoot MSEP>|t|1.74 0.0845.72 0.0002093.02-.12529343757.5430.08380.01440.009610582[95% Conf. Interval]1.982857710.291This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary.Suppose a CEO of a company makes 900 thousand dollars a year. What does the regressionpredict for the annual sales of this company (in millions of dollars)? (i.e. what is the value of salesfor this company)

Given the Regression Equation as follows, The Regression lines Equation of sales is, sales =…

Answered: We have data from 209 publicly traded…

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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Tire pressure (psi) and mileage (mpg) were recorded for a random sample of seven cars of thesame make and model. The extended data table (left) and fit model report (right) are based on aquadratic model What is the predicted average mileage at tire pressure x = 31?
The object in linear regression is to obtain an equation of a curve that minimizes the sum of squared vertical deviations of data points from the line. true or false?
Suppose that you are interested in the relationship between Reading and Writing scores. (c) Compute and interpret the R2 value for the relationship between Reading and Writing scores. (d) Interpret the value of the slope in the regression model predicting Reading scores from Writing scores. RDG WRTG 34 44 47 36 42 59 39 41 36 49 50 46 63 65 44 52 47 41 44 50 50 62 44 41 47 40 42 59 42 41 47 41 60 65 39 49 57 54 73 65 47 46 39 44 35 39 39 41 48 49 31 41 52 63 47 54 36 44 47 44 34 46 52 57 42 40 37 44 44 33 71 58 47 46 42 33 42 36 47 41 52 54 52 54 39 39 31 41 60 54 47 31 39 28 42 36 47 57 42 49 36 41 50 33 34 34 55 55 28 46 42 42 44 44 60 52 39 57 36 37 44 44 39 42 39 34 42 39 42 44 52 54 65 65 42 26 47 62 52 62 44 54 52 54 47 44 65 57 63 65 60 59 47 44 65 57 53 61 68 63 55 56 39 53 52 52 65 67 52 44 57 67 63…
Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue. The following is the descriptive statistics and regression output from Excel. Revenue People Income Competitors Price Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148…
9 students were surveyed to see what their age is and what their income level is. Find the equation of the line using linear regression. We want to predict their age using their income. age 18 24 38 22 19 35 28 19 27 income 456 786 835 855 645 244 587 1400 975 Just a side note, the 19 year old is making 1400, would be considered an outlier since they are making way more than everyone else. (y=-.0061x+34.9874 4 decimals) y=30.3709-.0064
We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…
Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue. The following is the descriptive statistics and regression output from Excel. Revenue People Income Competitors Price Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148 140…
Is it reasonable to interpret the y-intercept of the regression line? Why or why not? One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average for a home of this size? Use residuals and show your work to answer. footaget) ## (Intercept) 21.14411 136.31449 0.155 0.87954 ## footage 0.28388 0.08516 3.333 0.00667 ** ## Residual standard error: 71.64 on 11 degrees of freedom ## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573 ## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671

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