Construct a residual plot.161616161212-12-128.8.88-4-444-4-4.-4-4-8--8+-8--8+- 12- 12-12-12- 16- 16- 16-16248101214246 810121424681012142468101214Years of ExperienceYears of ExperienceYears of ExperienceYears of Experience(b) Do the assumptions about the error terms seem reasonable in light of the residual plot?O The plot suggests a funnel pattern in the residuals indicating that the error term assumptions appear reasonable.O The plot suggests a generally horizontal band of residual points indicating that the error term assumptions do not appear reasonable.O The plot suggests curvature in the residuals indicating that the error term assumptions appear reasonable.O The plot suggests a generally horizontal band of residual points indicating that the error term assumptions appear reasonable.O The plot suggests a funnel pattern in the residuals indicating that the error term assumptions do not appear reasonable. 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 = 80 + 4x.Years ofAnnual SalesSalespersonExperience($1,000s)1180397349244102103681117101198101239111171013136(a) Compute the residuals.Years ofAnnual SalesResidualsExperience($1,000s)1809749241026103811110119101231111713136

Solution: The estimated regression equation is y^= 80+4x

Answered: Construct a residual plot. 16- 16 16-…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

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The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
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
An article reported that for a regression of y = average SAT score on x = expenditure per pupil, based on data from n = 44 New Jersey school districts, a = 766, b = 0.015, r2 = 0.160, and se = 53.7. One observation in the sample was (9400, 897). What average SAT score would you predict for this district, and what is the corresponding residual?Predict average SAT score _______ Residual ________
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.…
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 97 4 4 107 5 6 103 6 8 101 7 10 119 8 10 123 9 11 127 10 13 136 (a) Compute the residuals. Years ofExperience Annual Sales($1,000s) Residuals 1 80 3 97 4 97 4 107 6 103 8 101 10 119 10 123 11 127 13 136 Construct a residual plot. A residual plot has 9 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 3 points below the line, 5 points above the line, and 1 point on the line. The points are plotted from left to right in an upward, diagonal direction starting from the lower left…
The following estimated regression model was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).ŷ = 30 + 0.7x1 + 3x2Also provided are SST = 1200 and SSE = 384.The yearly income of a 24-year-old female individual is _____.
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.
A surgery intern has conducted a study of the sleeping habits of her colleagues and has developed a following regression equation: y-hat = 6 + 0.1X, where X is the number of hours working on one shift, and Y is the number of hours sleeping at night after that shift. Yvette worked 10 hours and slept 8 hours. What is Yvette’s residual? 0.1 1 6 7
The following table represents the survey result from the seven online stores. Use linear regression to find the linear equation that is best fit for the data, width x equal to the online advertising. Round your coefficient to three decimal places. Online Store Online Advertising (in thousand dollars) Monthly e-commerce Sales (in thousand dollars) 1 1.5 368 2 1.7 340 3 2.8 665 4 5 954 5 1.3 331 6 2.2 556 7 1.3 376
The following table displays the mathematics test scores for a random sample of college students, along with their final SY16C grades. a. Fit the regression line y = a+bx to the data and interpret the results. b. Use the regression equation to determine the SY16C grade for a college student who scored60 on their achievement test. What would their SY16C grade be?
The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1x�^=�0+�1�. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 7171 6262 7878 9494 8383 8181 8080 9494 8585 6262 Grades on Final 8888 7979 8888 9191 8080 7070 7171 9393 6565 7777

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