Discussion question: Statistics in action

Apr 19th, 2015
KateS
Category:
Mathematics
Price: $20 USD

Question description

1.Please read the case in the attachment to answer what is the case about and describe the key variables by using couple sentences. 

u5c4fu5e55u5febu7167 2015-04-18 u4e0bu53485.19.47.png

u5c4fu5e55u5febu7167 2015-04-18 u4e0bu53485.46.47.png

2. Also following is one of my classmate's post, could you please review and make reflection on her post. Couple sentences will be enough. Also please use the above model to calculate 90% confidence interval for mean cost and 90% prediction interval for cost when DOTEST= 500 thousand dollars, and STATUS=0.

Classmate: First I ran a multiple regression with all the variables and used their P values to narrow down the relevant variables to: STATUS, DOTEST, and DAYSEST. I ran the regression for these three variables and found that the P value for DAYSEST increased from 0.058 to 0.122. If we are using a 95% confidence then the alpha value would be 0.05 and since 0.122 > 0.05 DAYSEST is no longer a relevant variable. This leaves STATUS and DOTEST as the remaining relevant variables. Below is the regression. 

The adjusted R-square shows at 97.52% which means that 97.52% change in cost can be explained by the regression model adjusted for sample size and number of variables. (COST = -20.5 + 166.3 STATUS + 0.93078 DOTEST)

The model is useful as determined by the P value of the regression equation (0.000 < 0.05) which is less than our alpha.

Regression Analysis: COST versus DOTEST, STATUSAnalysis of Variance

Source  DF  Seq SS  Contribution  Adj SS  Adj MS  F-Value P-ValueRegression  2  865106382  97.55%  865106382  432553191  4609.61  0.000  STATUS  1  8941884  1.01%  1068912  1068912  11.39  0.001  DOTEST  1  856164498  96.54%  856164498  856164498  9123.92  0.000Error  232  21770257  2.45%  21770257  93837  Lack-of-Fit  231  21769231  2.45%  21769231  94239  91.85  0.083  Pure Error  1  1026  0.00%  1026  1026Total  234  886876639  100.00%

Model Summary      S  R-sq R-sq(adj)  PRESS  R-sq(pred)306.329  97.55%   97.52%  23394862  97.36%

CoefficientsTerm  Coef  SE Coef  95% CI  T-Value  P-Value  VIFConstant  -20.5  26.8  (  -64.8,  23.8)  -0.77  0.445STATUS  166.3  49.3  (  85.0,  247.7)  3.38  0.001  1.02DOTEST  0.93078  0.00974  (0.91469, 0.94687)  95.52  0.000  1.02

Regression EquationCOST = -20.5 + 166.3 STATUS + 0.93078 DOTEST


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