In a study to examine the relationship between the time required to complete a construction project and several pertinent independent variables, an analyst compiled a list of four variables that might be useful in predicting the time to completion. These four variables were size of the contract, x1 (in $1000 unit), number of workdays adversely affected by the weather x2, number of subcontractors involved in the project x4, and a variable x3 that measured the presence (x3 = 1) or absence (x3 = 0) of a workers' strike during the construction. Fifteen construction projects were randomly chosen, and each of the four variables as well as the time to completion were measured.

An analysis of these data using a first-order model in x1, x2, x3, and x4 produced the following printout. Give a complete analysis of the printout and interpret your results. What can you say about the apparent contribution of x1 and x2 in predicting y?

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780513260236834 001010100100010 708424550053847 2 i 60 80 100 50 200 50 500 75 750 200 70 80 300 200 110 680505075078001 361 950005505071800 y-2161717349 2253 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9204 0.8471 0.7859 11.8450 15 ANOVA df MS Significance F Regression 4 Residual Total 7770.297 1942.574 13.846 1403.036 140.304 9173.333 0.000 10 14 Coefficients Standard Error t Stat Intercept x1 x2 x3 1.589 0.008 0.675 28.013 3.489 11.656 0.006 1.000 11.371 1.935 0.136 1.259 0.675 2.463 1.803 Pvalue 0.894 0.237 0.515 0.033 0.102 Normal Probability Plot of the Residuals (response is y) -30 -20 10 Residual 051 Residuals versus the Fitted Values tres ponse is y) 20 10- 10 20-t 10 20 30 40 Fitted Value 50 60 70 80