PREDICTING AN OUTCOME USING REGRESSION MODELS 1
MHA5017
Predicting an Outcome Using Regression Models
Capella University
Data Analysis for Health Care Decisions
MHA5017
Predicting an Outcome Using Regression Models
Multiple regression analysis is a way for us to understand more about the relationship
between several independent or predictor variables and a dependent or criterion variable (Kros &
Rosenthal, 2016). It also provides improvement of precision for estimation and prediction. In
this assignment, hospital administration needs to a make a decision on the amount of
reimbursement required to cover expected costs for next year. Based off the results of the
multiple regression analysis, a true statistical significance will help quantify whether a result is
likely due to chance or to some factor of interest (Gallo, 2016). The regression model will also
interpret the R square that takes into consideration the three independent variables of age, risk,
and satisfaction. These three independent variables will help explain the cost variable by
understanding if there is a relationship between the independent and independent variables. With
this analysis, the hospital administration will be able to predict the amount of reimbursement
needed to cover cost.
Interpret p-value and beta value
This model overall is significant because the p-value is 0.0 which is less than 0.05. A
significant result would be a p-value less than 0.05 and a non-significant result would be a p-
value greater than 0.5. The actual variables age, risk, and satisfaction p-values are statistically
