FIGURE 9.14 S AS output of a regression analysis of the construction project dat

Question

FIGURE 9.14 S AS output of a regression analysis of the construction project data using DEP VARIABLE: PROPIT ANALYBIS OF VARIANCE sqUARB VALUE PROB)2 4 77. 02981991 19,26745498 20, 440 0.0001 SQUARES ERROH 13 12. 24795767 0. 94215061 ROOT NS2 DEP KEAN R-SQUARE . 8628 ADJ R-SQ .8206 19. 85409 T FOR HO: PARAVETER-O STANDARD ERROR 2. 05205668 1. 17773388 PARAMETER ESTIMATE 19, 30495716 -1.48660196 -6.37145238 -0. 76224827 0, 22523749 0. 25377879 C31228 1.0VZR95% PREDICT UPPER95% PREDICT LOVER95% UPPER95% RESIDUAL 0. 6796 1.1823 6.3155 10.0280 -0.4481 .4481 4. 1638 6.9864 10. 4999 4. 3031 1.5000 3, 3667 SUM OF RESIDUALS SUM oF sQUARED RESIDUALS PREDICTED RESID 3S (PRESS) -1.489928-13 12. 24796 23.09611

Explanation / Answer

Part-a

CSIZE is not a significant predictor as t=-1.262, p-value=0.2290>0.05

SUPEXP is a significant predictor as t=-6.113, p-value=0.0001<0.05

CSIZESQ is a significant predictor as t=-3.340, p-value=0.0053<0.05

INTER is a significant predictor as t=6.766, p-value=0.0001<0.05

Part-b

WE have t= coefficient/SE(coefficient)=-0.75224827/0.22523749=-3.340

Critical t= tinv(0.05,13)=2.160

As calculated |t|>2.160, we reject the null hypothesis and conclude coefficient to be significant.

Part-c

p-value=TDIST(3.340,13,2)=0.0053

It is significant at 0.05 and 0.01 level while not significant at 0.001 level

Part-d

There is 0.53% probability to find coefficient in rejection region and as this is small so coefficient is significant.

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