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

Y_{p} = β_{0} + β_{1}x_{1} + ... + β_{k}x_{k} + σΦ^{-1}(p)

- Prediction (Y
_{p}): log failure time (Weibull, exponential, lognormal, and loglogistic models) or failure time (normal, extreme value, and logistic models). - Predictors (x
_{1}, x_{2}... x_{k}): the predictor variables, which can be either continuous or categorical. - Constant (β
_{0}): the value of Y_{p}(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 (β
_{1}, β_{2},... , β_{k}): 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 p^{th}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.

Regression Table
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
Design
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(Y_{p}) = 6.8731 – 0.0565899(Weight) + (1.0/5.79286)Φ^{-1}(p)

The estimated model for the standard compressor cases is: log(Y_{p}) = (6.8731 – 0.705643) – 0.0565899(Weight) + (1.0/5.79286)Φ^{-1}(p)

Where:

- Y
_{p}: the failure time for the compressor case failures - Weight: weight of the projectile thrown at the engine
- Φ
^{-1}(p): the pth quantile from the standardized extreme value distribution (for more information, go Methods and formulas for equations in Regression with Life Data and click "Quantile function").