Find confidence intervals for predictors of the `readmissiontimes`

data set. The response variable is `ReadmissionTime`

, which shows the readmission times for 100 patients. The predictor variables are `Age`

, `Sex`

, `Weight`

, and `Smoker`

, the smoking status of each patient. A 1 indicates the patient is a smoker, and a 0 indicates the patient does not smoke. The column vector `Censored`

contains the censorship information for each patient, where 1 indicates censored data, and 0 indicates the exact readmission times are observed. (This data is simulated.)

Load the data.

Use all four predictors for fitting a model.

Fit the model using the censoring information.

View the point estimates for the `Age`

, `Sex`

, `Weight`

, and `Smoker`

coefficients.

ans = *4×1*
0.0184
-0.0676
0.0343
0.8172

Find 95% confidence intervals for these estimates.

ci = *4×2*
-0.0139 0.0506
-1.6488 1.5136
0.0042 0.0644
0.2767 1.3576

The `Sex`

coefficient (second row) has a large confidence interval, and the first two coefficients bracket the value 0. Therefore, you cannot reject the hypothesis that the `Age`

and `Sex`

predictors are zero.