The following estimated regression equation based on 30 observations was presented. ŷ= 17.6+ 3.8x₁2.3x₂ + 7.6x3 + 2.7x4 The values of SST and SSR are 1,801 and 1,756, respectively. (a) Compute R². (Round your answer to three decimal places.) R² = (b) Compute R. (Round your answer to three decimal places.) (c) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation.

The following estimated regression equation based on 30 observations was presented. ŷ= 17.6+ 3.8x₁2.3x₂ + 7.6x3 + 2.7x4 The values of SST and SSR are 1,801 and 1,756, respectively. (a) Compute R². (Round your answer to three decimal places.) R² = (b) Compute R. (Round your answer to three decimal places.) (c) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.CR: Chapter Review Exercises

Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...

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Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300
For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.
For the following exercises, consider the data in Table 5, which shows the percent of unemployed ina city of people 25 years or older who are college graduates is given below, by year. 40. Based on the set of data given in Table 6, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient to three decimal places.
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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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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.093 b=38.49 r²=0.806404 r=-0.898 Use this to predict the number of situps a person who watches 1 hours of TV can do (to one decimal place)
Given the following multiple regression equation : y = 122+ 2 x1 + 1 x2, where Y:Total expenses, X1: income and X2 wealth . If a person has an income of $1132 and a wealth of $2563. Then their total expenses would be : Select one: Oa. -4705.00 Ob. 6380.00 OC. 4949.00 Od. 4827.00 Next page ACTIVITY NEXT ACTIVITY Jump to...
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=ax+b a=-0.788 b=22.872 r2=0.388129 r=-0.623 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)
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