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Test interviews of two personnel evaluation techniques are available, the first requires a two-hour test interview while the second can be completed in less than an hour. The table below is the output of the regression analysis of the 15 individuals who took both tests: a.Using the method of least squares, the estimated regression will be? b.Suppose you must test the hypothesis at 5% significance level that  versus  using t-test. The value of J will be?

Test interviews of two personnel evaluation techniques are available, the first requires a two-hour test interview while the second can be completed in less than an hour. The table below is the output of the regression analysis of the 15 individuals who took both tests: a.Using the method of least squares, the estimated regression will be? b.Suppose you must test the hypothesis at 5% significance level that  versus  using t-test. The value of J will be?

MATLAB: An Introduction with Applications
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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Test interviews of two personnel evaluation techniques are available, the first requires a two-hour test interview while the second can be completed in less than an hour. The table below is the output of the regression analysis of the 15 individuals who took both tests:

a.Using the method of least squares, the estimated regression will be?

b.Suppose you must test the hypothesis at 5% significance level that  versus  using t-test. The value of J will be?

Regression Statistics Multiple R 0.953935025 R Square M Adjusted R Square 0.903068342 Standard Error 3.134808415 Observations 15 ANOVA df ss MS F Significance F 1291.582 1291.582 131.4316573 3.60668E-08 Regression Residual 13 127.7513 9.827024 Total 14 1419.333 Coefficients Standard Error Stat P-value Lower 95% Upper 95% Intercept 11.66533278 5.295713 2.20279 0.046260025 23.1060256 -0.224639973 Test 1 (X) 0.755016772 0.065858 3.60668E-08 N
Transcribed Image Text:Regression Statistics Multiple R 0.953935025 R Square M Adjusted R Square 0.903068342 Standard Error 3.134808415 Observations 15 ANOVA df ss MS F Significance F 1291.582 1291.582 131.4316573 3.60668E-08 Regression Residual 13 127.7513 9.827024 Total 14 1419.333 Coefficients Standard Error Stat P-value Lower 95% Upper 95% Intercept 11.66533278 5.295713 2.20279 0.046260025 23.1060256 -0.224639973 Test 1 (X) 0.755016772 0.065858 3.60668E-08 N
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