The following estimated regression equation based on 30 observations was presented. ŷ = 17.6 + 3.8x, - 2.3x, + 7.6x3 + 2.7x4 The values of ST and SSR are 1,807 and 1,755, respectively. (a) Compute R?. (Round your answer to three decimal places.) R2 = (b) Compute R_?. (Round your answer to three decimal places.) R_2 = (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 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 small 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 + 3.8x, - 2.3x, + 7.6x3 + 2.7x4 The values of ST and SSR are 1,807 and 1,755, respectively. (a) Compute R?. (Round your answer to three decimal places.) R2 = (b) Compute R_?. (Round your answer to three decimal places.) R_2 = (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 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 small 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.

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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K The accompanying table lists the numbers of Internet users per 100 people and numbers of scientific award winners per 10 million people for different countries. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Use a significance level of α = 0.05. Click to view the data on Internet users and scientific award winners. UA. Award Winners 40- 32- 24- 16- 8- 0+ 0 20 40 60 80 100 Internet Users M T 1 D L The linear correlation coefficient is r= (Round to three decimal places as needed.) Determine the null and alternative hypotheses. Ho: P H₁: P (Type integers or decimals. Do not round.) The test statistic is t = (Round to two decimal places as needed.) The P-value is (Round to three decimal places as needed.) UB. Award Winners 43468 40- Because the P-value of the linear correlation coefficient is Internet users and…
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