Where applicable, indicate who are the components of the matrix formulation of the RLM model considered.4.16. You are given the following matricescomputed for a regression analysis:9136 269136 2,114 4,176260X'X= =3,583269 4,176 8,257 7,104260 3,583 7,104 12,27645648X'y =1,2831,8219.610 0.008 -0.279 -0.044(X'X)-1)=0.008 0.509 -0.258 0.001-0.279 -0.258 0.139 0.0010.001 0.0003-0.044 0.001-1.1634610.135270= (X'X)-(X'y)=0.0199500.121954y'y = 285a. Use these results to construct the analysisof variance table.b. Give the computed regression equationand the standard errors of the regressioncoefficients.c. Compare each estimated regressioncoefficient to its standard error and use thet test to test the simple hypotheses thateach individual regression coefficient isequal to zero. State your conclusions aboutB1, B2, and B.

To begin, the given matrices represent the results of a regression analysis. The matrix $ X'X $…

Answered: Where applicable, indicate who are the…

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 number of initial public offerings of stock issued in a 10-year period and the total proceeds of these offerings (in millions) are shown in the table. The equation of the regression line is y = 48.314x+18,056.64. Complete parts a and b. 67 197 152 Issues, x 415 458 681 481 483 377 66 Proceeds, 19,061 29,468 43,357 30,436 65,907 65,385 20,287 10,145 31,744 27,934 ly (a) Find the coefficient of determination and interpret the result. 0.284 (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? The coefficient of determination is the fraction of the variation in proceeds that is unexplained and is due to other factors or sampling error. The remaining fraction of the variation is explained by the variation in issues. The coefficient of determination is the fraction of the variation in proceeds that can be explained by the variation in issues. The remaining fraction of the variation is unexplained and is due to other factors or to sampling…
7. Find slop of a linear regression model for the following data: x = [1, 2, 3, 4, 5, 6, 7] z = [ 1.40, 3.78, 4.41, 4.60, 8.40, 8.64, 12.81]. -1.7 -0.5 0.5 O 1.7 CS Scanned with CamScannel
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.9 8.4 6.8 3.8 2.9 2.7 2.6 0.5 14 11.1 9.6 7.3 6.7 6.5 6.3 4.7 x = thousands of automatic weapons y = murders per 100,000 residents %3D Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 2.1 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 10.9 thousand automatic weapons? Answer = Round to 3 decimal places. %3D
I just need help on on parts H, i and J. The regression line for part G is on the first page. Thank you.
18. Give the following data, which is the equation of the regression line? 3 4 12 Y 8. 6. 9. 12 C. y = 4.98 + 0.725 d. y = 4,88 + 0.525 a. y = 4.88+ 0.625x %3D b. y = 4.98 +0.425x 19. Which of the following gives the best fit of the line for a given data? a. y = 2+ 6.5x, r = 0.65 b. y = 6.05 + 0.825x, r = 0.64 c. y = -1.10 + 9x, r = -0.72 d. y = 100 + 75x, r = 0.50
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 = 81 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 80 3 97 3 102 4 4 102 103 6. 8 101 10 119 8 10 123 9. 11 127 10 13 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did…

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