SUMMARY OUTPUT Regression Statistics Multiple R 1 R Square 1 Adjusted R Square 65535 Standard Error 0 Observations 4 ANOVA df SS MS F Significance F Regression 3 19235.23924 6411.74641 #NUM!#NUM! Residual 0 0 65535 Total 3 19235.23924 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1412.72036 0 65535 #NUM! 1412.72036 1412.72036 1412.72036 1412.72036 X Variable 1 -616.63372 0 65535 #NUM! -616.63372 -616.63372 -616.63372 -616.63372 X Variable 2 757.72152 9 0 65535 #NUM! 757.721529 757.721529 757.721529 757.721529 X Variable 3 -506.28783 0 65535 #NUM! -506.28783 -506.28783 -506.28783 -506.28783 RESIDUAL OUTPUT PROBABILITY OUTPUT Observation Predicted Y Residuals Standard Residuals Percentile Y 1 1.653 -2.47358E-13 -0.6238259 12.5 1.653 2 3.677 3.20188E-13 0.80750171 37.5 3.677 3 167.6 5.40012E-13 1.36188915 62.5 19.39 4 19.39 1.27898E-13 0.32255269 87.5 167.6
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From the given information, we have: Intercept: 1412.72036 X Variable 1: -616.63372 X Variable 2: 757.721529 X Variable 3: -506.2878 Show moreā¦
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SUMMARY OUTPUT Regression Statistics Multiple R 0.535927511 R Square 0.287218297 Adjusted R Square 0.26625413 Standard Error 6938.734668 Observations 36 ANOVA df SS MS F Significance F Regression 1 659621859.2 6.6E+08 13.70044 0.000755105 Residual 34 1636965319 48146039 Total 35 2296587178 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 819247.4551 51893.25883 15.78717 3.22E-17 713787.6648 924707.245 713787.665 924707.2454 Advertising Expenses 3.677554271 0.993554859 3.70141 0.000755 1.658407864 5.69670068 1.65840786 5.696700677 The ______ the value of adjusted r-squared, the greater the ___ of the model. multiple choice 2 lower; capability greater; fit lower; fit greater; capability
Sri K.
Consider the following regression analysis report: Regression Statistics Multiple R: 0.977865 R Square: 0.956221 Adjusted R Square: 0.95492 Standard Error: 32.85796 Observations: 209 ANOVA df SS MS F Significance F Regression 6 4763462 793910.3 2.8E-134 Residual 218088.4 Total 208 4981550 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -21.4174 3.613163 -5.92761 1.31E-08 -28.5418 -14.2931 MMIN 0.004862 0.00115 4.227276 3.58E-05 0.002594 0.007129 MMAX 0.003048 0.000403 7.560398 1.38E-12 0.002253 0.003842 CACH 0.0781 0.079442 0.326731 -0.07854 0.234743 CHMIN -0.09145 0.468396 -0.19523 0.845408 -1.01502 0.832126 CHMAX 0.292503 0.132665 2.20483 0.028595 0.030918 0.554088 PRP 0.605678 0.037818 16.01576 8.28E-38 0.53111 0.680246 What is the value of F*?
Summary Output Regression Statistics Multiple R: 0.7732 R Square: 0.5978 Adjusted R Square: 0.5476 Standard Error: 3.0414 Observations: 10 ANOVA df SS MS F Significance F Regression: 1 110 110 11.892 0.009 Residual: 8 74 9.25 Total: 9 184 Coefficients Standard Error t Stat P-value Intercept: 39.222 5.942 6.600 0.000 x: -0.556 0.161 -3.448 0.009 e. 59.783% of the variability in Y is explained by the variability in X. a. What has been the sample size for the above? b. Perform a t test and determine whether or not X and Y are related. Let ĆĀ± = 0.05. c. Perform an F test and determine whether or not X and Y are related. Let ĆĀ± = 0.05. d. Compute the coefficient of determination. e. Interpret the meaning of the value of the coefficient of determination that you found in d. Be very specific.
Krishna G.
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