CU Multiple Regression Model

Regression is an important statistical technique for determining the relationship between an outcome (dependent variable) and predictors (independent variables). Multiple regression evaluates the relative predictive contribution of each independent variable on a dependent variable. The regression model can then be used for predicting an outcome at various levels of the independent variables. For this assignment, you will perform multiple regression and generate a prediction to support a health care decision.


Download Assignment 3 Dataset. 

The dataset contains the following variables:

  • Cost? (hospital cost in dollars)?.
  • Age (patient age in years)?.
  • Risk (count of patient risk factors).
  • Satisfaction (patient satisfaction score percentile rank)?.

Walkthrough: You may view the Predicting an Outcome Using Regression Models Walkthrough, linked in the Resources, to help you prepare for your assignment.


Hospital administration needs to make a decision on the amount of reimbursement required to cover expected costs for next year. For this assignment, using the information on hospital discharges from last year, perform multiple regression on the relationship between hospital costs and patient age, risk factors, and patient satisfaction scores, and then generate a prediction to support this health care decision. Write a 3–4-page analysis of the results in a Word document and insert the test results into this document (copied from the output file and pasted into a Word document). Refer to the “Copy From Excel to Another Office Program” resource for instructions. 

Grading Criteria

The numbered assignment instructions outlined below correspond to the grading criteria in Predicting an Outcome Using Regression Models Scoring Guide, so be sure to address each point. You may also want to review the performance-level descriptions for each criterion to see how your work will be assessed

  1. Perform the appropriate multiple regression using a dataset.
  2. Interpret the statistical significance and effect size of the regression coefficients of data analysis.
    • Interpret p-value and beta values.
  3. Interpret the fit of the regression model for the prediction of data analysis.
    • Interpret R-squared and goodness of fit.
  4. Apply the statistical results of the multiple regression of data analysis to support a health care decision.
    • Generate a prediction with the regression equation.
  5. Write a narrative summary that includes practical, administration-related implications of the multiple regression.
  6. Write clearly and concisely, using correct grammar, mechanics, and APA formatting.

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