Given are data for two variables, x and y.X 611 15 18205713 2030(a) Develop an estimated regression equation for these data. (Round your numerical values to two decimal places.)ý =(b) Compute the residuals. (Round your answers to two decimal places.)Residuals117151318202030(c) Develop a plot of the residuals against the independent variable x.6.6.4420--2--2--4-4-6--641015202510152025х6.6.4-2-2-2-21.-4-4-6--6-510152025510152025Do the assumptions about the error terms seem to be satisfied?O The plot suggests a funnel pattern in the residuals indicating that the error term assumptions are not satisfied.The plot suggests curvature in the residuals indicating that the error term assumptions are satisfied.O The plot suggests a generally horizontal band of residual points indicating that the error term assumptions are satisfied.The plot suggests curvature in the residuals indicating that the error term assumptions are not satisfied.O The plot suggests a generally horizontal band of residual points indicating that the error term assumptions are not satisfied.(d) Compute the standardized residuals. (Round your answers to two decimal places.)Standardized ResidualsResidualsOO000ResidualsResidualsResiduals Do the assumptions about the error terms seem to be satisfied?O The plot suggests a funnel pattern in the residuals indicating that the error term assumptions are not satisfied.The plot suggests curvature in the residuals indicating that the error term assumptions are satisfied.The plot suggests a generally horizontal band of residual points indicating that the error term assumptions are satisfied.O The plot suggests curvature in the residuals indicating that the error term assumptions are not satisfied.O The plot suggests a generally horizontal band of residual points indicating that the error term assumptions are not satisfied.(d) Compute the standardized residuals. (Round your answers to two decimal places.)Standardized Residuals6.1171513182030(e) Develop a plot of the standardized residuals against ý.33.211--1-1-2-2--3-05 10-3-15202530510152025303-22-1-1-1-2-2-3-310152025301015202530What conclusions can you draw from this plot?O The standardized residual plot has a different shape than the original residual plot. The plot suggests curvature in theresiduals indicating that the error term assumptions are satisfied.O The standardized residual plot has the same shape as the original residual plot. The plot suggests curvature in the residualsindicating that the error term assumptions are not satisfied.O The standardized residual plot has a different shape than the original residual plot. The plot suggests a funnel pattern in theresiduals indicating that the error term assumptions are not satisfied.O The standardized residual plot has the same shape as the original residual plot. The plot suggests a generally horizontal bandof residual points indicating that the error term assumptions are not satisfied.O The standardized residual plot has a different shape than the original residual plot. The plot suggests a generally horizontalband of residual points indicating that the error term assumptions are satisfied.Standardized ResidualsStandardized Residuals20Standardized ResidualsStandardized Residuals

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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 table shows the numbers of new-vehicle sales (in thousands) in the United States for Company A and Company B for 10 years. The equation of the regression line is y = 0.991x + 1,222.81. Complete parts (a) and (b) below. D New-vehicle sales (Company A), x New-vehicle sales (Company B), y 4,149 3,923 3,566 3,400 3,266 3,076 2,868 2,485 1,952 2,066 4,912 4,871 4,827 4,721 4,672 4,474 4,684 3,822 2,956 2,754 (a) Find the coefficient of determination and interpret the result. 12²=0 (Round to three decimal places as needed.) 1
The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each day in degrees Celsius. Complete parts a and b below. Temperature (°C) 20 Demand 49 11 23 32 36 19 40 5 17 10 21 16 44 20 D 34 a. Calculate the slope and y-intercept for the linear regression equation for these data. ŷ=+* (Round to two decimal places as needed.)
A statistical program is recommended. The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly Television Gross Newspaper Advertising Advertising ($1,000s) ($1,000s) Revenue ($1,000s) 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 1 (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) y = 88.64 + 1.60x1 X (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s, x₂ represent the amount of…
The table shows the average weekly wages (in dollars) for state government employees and federal government employees for 8 years. The equation of the regression line is y = 1.493x - 83.403. Complete parts (a) and (b) below. A Average Weekly Wages (state), x Average Weekly Wages (federal), y 764 1003 766 1048 791 1119 (a) Find the coefficient of determination and interpret the result. r² = 0 (Round to three decimal places as needed.) 800 1152 843 1201 887 1250 924 1277 939 1306
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x Test score, y 4 (a) x = 2 hours (c) x = 13 hours (b) x= 2.5 hours (d) x = 4.5 hours 2 3 5 40 45 52 49 63 70 Find the regression equation. ý =x+ (O (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. O A. OB. OC. OD. 80- 80- Hours studying Hours studying Hours studying Hours studying (a) Predict the value of y for x= 2. Choose the correct answer below. O A. 52.2 О В. 49.2 ОС. 64.2 O D. not meaningful (b) Predict the value of y for x = 2.5. Choose the correct answer below. O A. 115.4 О В. 52.2…
The following data shows memory scores collected from adults of different ages. Age (X) Memory Score (Y) 25 10 32 10 39 9 48 9 56 7 Use the data to find the regression equation for predicting memory scores from age. The regression equation is: Ŷ = 4.33X + 0.11 Ŷ = -0.11X + 4.33 Ŷ = -0.11X + 13.26 Ŷ = -0.09X + 5.4 Ŷ = -0.09X + 12.6 Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 28 For the calculations, leave two places after the decimal point and do not round: Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 43 For the calculations, leave two places after the decimal point and do not round: Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 50 For the calculations, leave two places after the decimal point and do not round:
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. The equation of the regression line is y = 1.597x- 169.203. Wages (state), x Wages (federal), y 742 775 783 802 842 890 912 939 949 954 1,002 1,039 1,097 1,145 1,199 1,242 1,271 1,299 1,335 1,398 Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. There is a 90% chance that the predicted average weekly wages of federal government employees is between S and S given a state average weekly wage of $825. O B. We can be 90%…
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 95% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $895. The equation of the regression line is y = 1.454x- 36.984. Wages (state), x Wages (federal), y 712 778 789 801 843 878 927 937 939 958 1,014 1,054 1,116 1,146 1,191 1,248 1,270 1,302 1,335 1,401 Construct and interpret a 95% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $895. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. We can be 95% confident that when the average weekly wages of state government employees is $895, the average weekly wages of federal government employees will be between $ and…
The following table gives the amount spent on cellular service. Date Cellular service revenue(in billions) 2011 1.01 2012 1.05 2013 1.09 2014 1.11 Plot the data points. (Let tbe the number of years since 2011 and C the amount of cellular service revenue, in billions of dollars.) CORRECT (b) Find the equation of the regression line. (Let t be the number of years since 2011 and C the amount of cellular service revenue, in billions of dollars. Round the regression line parameters to three decimal places.) C(t) = C(t) = 0.034t+1.014 CORRECT Add its graph to the plotted data. CORRECT (c) In 2015, $1.14 trillion was spent on cellular service. If you had been a financial strategist in 2014 with only the data in the table above available, what would have been your prediction for the amount spent on cellular service in 2015? (Round your answer to two decimal places.) billion dollars CORRECT…
B b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? The scatter diagram indicates a positive linear relationship between a = average number of passing yar and y = the percentage of games won by the team. c. Develop the estimated regression equation that could be used to predict the percentage of games won given the avera passing yards per attempt. Enter negative value as negative number. WinPct =| |)(Yds/Att) (to 4 decimals) d. Provide an interpretation for the slope of the estimated regression equation (to 1 decimal). The slope of the estimated regression line is approximately So, for every increase : of one yar number of passes per attempt, the percentage of games won by the team increases by %. e. For the 2011 season, the average number of passing yards per attempt for the Kansas City Chiefs was was 5.5. Use th regression equation developed in part (c) to predict the percentage of games won by the Kansas City Chiefs.…
The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each day in degrees Celsius. Complete parts a and b below. Temperature (°C) 22 13 22 20 5 Demand 30 35 38 16 13 16 21 O 21 41 31 47 a. Calculate the slope and y-intercept for the linear regression equation for these data. ŷ=+x (Round to two decimal places as needed.) b. Predict the demand for hot dogs on a day with a temperature of 7°C. The predicted demand for hot dogs on a day with a temperature of 7°C is (Round to the nearest integer as needed.) of
The following table shows the approximate amount of trash produced in an industrialized country from 1980 to 2000. Let x represent the year after 1980 (1980 is year 0) and y represent the amount of trash (millions of tons). Year Million Tons 1980 150 1990 202 2000 220 (a) Draw the graph for this data. (b) Find the equation of the regression line for the data. (Round your answers to one decimal place.)y = x + (c) Use the equation to predict the amount of trash y that will be produced in 2010 and 2015. (Round your answers to one decimal place.) 2010: _______million tons 2015: _______ million tons

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