Full Model	Ridge Regression	Ridge Regression
(Intercept)	116.3	119.3	158.5
Age	1.1	1.0	0.3
Asthma	0.7	0.5	0.4
CAD	17.0	16.9	10.0
CHF	8.0	8.6	9.4
CKD	19.5	19.8	12.8
COPD	-5.4	-4.0	4.5
Depression	31.1	29.9	10.3
Diabetes	11.0	10.9	6.9
ESRD	31.1	29.3	11.3
Female	-2.5	-2.1	0.2
HIV	70.7	68.2	23.2
Hyperlipidemia	19.9	19.5	10.2
Hypertension	-8.2	-5.5	5.8
Injury	-23.6	-22.2	-5.9
Obesity	8.9	8.7	6.0
Substance_Abuse	40.2	38.2	10.5
Wait_Time	0.7	0.7	0.2

RMSE - Ridge Regression,

120.57

To compute the test error for the ridge.min model,

1. compute the predicted values of the response by applying the ridge.min estimated above to the test_set

x=model.matrix(LOV~ .,data = test_set)[,-1] predicted=predict(ridge.min,newx=x)

1. compare the observed values of the response in the test set, test_set$LOV, to the predicted values of the response obtained in step 1 (for reference below save the result as rmse_ridge.min)

rmse_ridge.min=round(rmse(test_set$LOV, predicted),2) knitr::kable(rmse_ridge.min, col.names =c("RMSE -- Ridge Regression, $\\lambda_{min}$"))

RMSE - Ridge Regression,