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Regression Modeling Analysis:
Determining the Number of Days a Patient is in the Hospital

Rochester Institute of Technology
Executive Masters of Business Administration
CLASS XX/TEAM III
CHRIS HAJECKI, CHUCK D’AGNOSTINO,
BARBARA STEPHENS, MARY KATE SCANLON
NOVEMBER 17th, 2012

Abstract:
Objectives: To identify factors associated with the number of days spent in the hospital. Design: A multiple regression model applied to data from a sample size. We chose this design because it shows multiple correlations concurrently between all of the independent variables and how they predict the number of days a patient spends in the hospital. We began by running a regression model using all variables. We removed the variables that had P-values over .05, indicating that they were insignificant. We were surprised that “age at first claim was not significant”. We ran correlations between the significant independent variables that showed that they were not correlated. Then we ran another regression model with the variables that had P-values under.05. The P-value for the f-test showed that there was a difference in means of at least one of the independent variables for both regression models. Then, we considered that there must be some correlation with “age at first claim”. We sorted the data set into two subsets, one with 0-39 year olds, and one with 40+ year olds. We ran a regression for both age groups with “visit count” and “drug count”. Then we ran another one with “visit count,” “drug count” and “age at first claim”. Breaking the data into subsets improved the goodness of fit (Adj. R-SQ) for the younger group and decreased the goodness of fit by .1 for the older group. Again adding age at first claim did not have significance. Then we ran correlations between “visit count” and “drug count” with the two subsets and found no co-correlation. The p-value of the f-test is below .05 so the test has some degree of significant. For each additional visit count there...
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