The table estimates the best fitting regression equation for the model. The regression equation takes the following general form:
Prediction = constant + coefficient(predictor) + ... + coefficient(predictor) + scale (quantile function) or
Yp = β0 + β1x1 + ... + βkxk + σΦ-1(p)
- Prediction (Yp): log failure time (Weibull, exponential, lognormal, and loglogistic models) or failure time (normal, extreme value, and logistic models).
- Predictors (x1, x2 ... xk): the predictor variables, which can be either continuous or categorical.
- Constant (β0): the value of Yp (failure time or log failure time) when all of the explanatory variables are equal to zero and the percentile of the quantile function is 0.
- Coefficient (β1, β2,... , βk): the amount by which Y changes when the corresponding explanatory variable (x) increases by one unit and all other explanatory variables are held constant.
- Scale (σ): the scale parameter. For Weibull and exponential, scale = 1.0/shape.
- Quantile function (Φ-1(p): the pth quantile of the standardized life distribution.
This model might not provide a good fit to the data. To assess model fit, check the assumptions of the model by using the probability plot of the standardized residuals and the Cox-Snell residuals.
Standard 95.0% Normal CI
Predictor Coef Error Z P Lower Upper
Intercept 6.68731 0.193766 34.51 0.000 6.30754 7.06709
Standard -0.705643 0.0725597 -9.72 0.000 -0.847857 -0.563428
Weight -0.0565899 0.0212396 -2.66 0.008 -0.0982187 -0.0149611
Shape 5.79286 1.07980 4.02001 8.34755
Log-Likelihood = -88.282
The estimated model for the new compressor cases is: log(Yp) = 6.8731 – 0.0565899(Weight) + (1.0/5.79286)Φ-1(p)
The estimated model for the standard compressor cases is: log(Yp) = (6.8731 – 0.705643) – 0.0565899(Weight) + (1.0/5.79286)Φ-1(p)