A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 82 + 4x.Years ofExperienceAnnual SalesSalesperson($1,000s)8023.9710244107103101101198.101286.111271013136(a) Compute the residualsYears ofAnnual SalesResiduaisExperience(S1,000s)974.10210710310110119101261112713136Construct a residual plot. 1616161212128--4'-4-4-8-12-12-16-1610121410 12Years of Experience14101214Years of ExperienceYears of Experience16124.-4-84-12-166 8 10 12 14Years of Experience(b) Do the assumptions about the errer terms seem reasonable in light of the residual plot?The plat suggests a generally horzontal band of residual points indicating that the error term assumptions apear reasonableThe plot suggests curvature in the residuals indicating that the errer tem asumptions appear reasonatleThe plot seggests a funnel patterm in the residuals indicating that the error term assumptions appeer reasonableThe plot suggests a generaly horizontal bend of residual points indicating that the error term assumptions do not appear reasonableThe plot suggests a funnel pattern in the residuals indicating that the enror term assumgtions do not agpear reasonabie.spenppagSenpsay

Solution: The estimated regression equation is y^= 82+4x

Answered: Years of Experience Salesperson Annual…

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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For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
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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.
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A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ = 81 + 4x. Salesperson Years ofExperience Annual Sales($1,000s) 1 1 80 2 3 97 3 4 92 4 4 107 5 6 103 6 8 111 7 10 119 8 10 128 9 11 117 10 13 136 (a) Compute the residuals. Years ofExperience Annual Sales($1,000s) Residuals 1 80 3 97 4 92 4 107 6 103 8 111 10 119 10 128 11 117 13 136 Construct a residual plot. A residual plot has 10 points plotted on it. The horizontal axis ranges from 0 to 14 and is labeled: Years of Experience. The vertical axis ranges from −16 to 16 and is labeled: Residuals. There is a horizontal line that spans the graph at 0 on the vertical axis. There are 6 points below the line and 4 points above the line. The points are between 1 to 13 on the horizontal axis and appear to vary randomly between −8 to 10 on the vertical axis.…
Suppose the following data were collected from a sample of 1515 CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARYi=b0+b1EXPERIENCEi+b2SERVICEi+b3INDUSTRIALi+eiSALARY�=�0+�1EXPERIENCE�+�2SERVICE�+�3INDUSTRIAL�+��. Is there enough evidence to support the claim that on average, CEOs in the service sector have lower salaries than CEOs in the financial sector at the 0.010.01 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data CEO Salaries Salary Experience Service (1 if service sector, 0 otherwise) Industrial (1 if industrial sector, 0 otherwise) Financial (1 if financial sector, 0 otherwise) 144225144225 1010 11 00 00 187765187765 2020 00 00 11 142500142500 66 11 00 00 169650169650 2828 11 00 00 167250167250 3131 00…
An investigation into the relationship between an adolescent mother's age x in years and the birth weight y of her baby in grams yielded the regression equation y= - 1163.45 + 245.15x as well as r = .88369, r2= .78091, SSE = 337212.45, and s= 205.30844 1) What is the predicted birth weight for a baby brn to a 17 year old woman? 2) What is the propotion of the variability in the weights of babies born to adolescent mothers that is accounted for by the mother's age? 3) For every additional year in the mother's age that mean birth weight of the baby? (a) increases by about 245g (b) decreases by about 245g (c) increases by about 1163g (d) increases by about 1163g (e) changes by an amount that cannot be determined from the information given.

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Years of Experience Salesperson Annual Sales ($1,000s) 80 2. 97 102 4 107 6. 103 101 10 119 10 128 9. 11 127 10 13 136