19 Aug Predicting an Outcome Using Regression Models Due: Sun Aug 27, 2023 11:59pmDue: Sun Aug 27, 2023 11:59pmUngraded, 200 Possible Points200 Possible PointsAttemptIn ProgressNEXT UP: Submit As
Predicting an Outcome Using Regression Models
Due: Sun Aug 27, 2023 11:59pmDue: Sun Aug 27, 2023 11:59pmUngraded, 200 Possible Points200 Possible PointsAttemptIn ProgressNEXT UP: Submit AssignmentAdd CommentDetails
Introduction
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 healthcare decision.
Preparation
Download Assignment 3 Dataset [XLSX] Download Assignment 3 Dataset [XLSX].
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, to help you prepare for your assignment.
Instructions
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 healthcare 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 rubric, 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
- Perform the appropriate multiple regression using a dataset.
- Interpret the statistical significance and effect size of the regression coefficients of data analysis.
- Interpret p-value and beta values.
- Interpret the fit of the regression model for the prediction of data analysis.
- Interpret R-squared and goodness of fit.
- Apply the statistical results of the multiple regression of data analysis to support a health care decision.
- Generate a prediction with the regression equation.
- Write a narrative summary that includes practical, administration-related implications of the multiple regression.
- Write clearly and concisely, using correct grammar, mechanics, and APA formatting.
Additional Requirements
Your assignment should also meet the following requirements:
- Written communication: Write clearly, accurately, and professionally, incorporating sources appropriately.
- Length: 3–4 pages.
- APA format: Cite your sources using the current Evidence and APALinks to an external site. format.
- Font and font size: Times Roman, 12 point.
: Predicting an Outcome Using Regression Models
Due: Sun Aug 27, 2023 11:59pmDue: Sun Aug 27, 2023 11:59pm
Ungraded, 200 Possible Points200 Possible Points
Attempt
In Progress
NEXT UP: Submit Assignment
Add Comment
Details
Introduction
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 healthcare decision.
Preparation
Download Assignment 3 Dataset [XLSX] Download Assignment 3 Dataset [XLSX] .
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 , to help you prepare for your assignment.
Instructions
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 healthcare 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 rubric, 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.
Additional Requirements
Your assignment should also meet the following requirements:
· Written communication: Write clearly, accurately, and professionally, incorporating sources appropriately.
· Length: 3–4 pages.
· APA format: Cite your sources using the current Evidence and APALinks to an external site. format.
· Font and font size: Times Roman, 12 point.
View Rubric
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Week 7 Assignment – Predicting an Outcome Using Regression Models |
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Week 7 Assignment – Predicting an Outcome Using Regression Models |
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Criteria |
Ratings |
Pts |
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Perform the appropriate multiple regression using a dataset. view longer description |
34 to >28.9 pts DISTINGUISHED Performs the appropriate multiple regression using a dataset. Provides concise, logical reasoning for the multiple regression. 28.9 to >23.8 pts PROFICIENT Performs the appropriate multiple regression using a dataset. 23.8 to >0 pts BASIC Incorrectly performs the appropriate multiple regression. 0 pts NON_PERFORMANCE Does not perform the appropriate multiple regression using a dataset. |
/ 34 pts |
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Interpret the statistical significance and effect size of the regression coefficients of a data analysis. view longer description |
34 to >28.9 pts DISTINGUISHED Interprets the statistical significance and effect size of the regression coefficients of a data analysis and ensures the interpretation is complete, provides a perceptive and clearly articulated conclusion, and includes an assessment of caveats and limitations. 28.9 to >23.8 pts PROFICIENT Interprets the statistical significance and effect size of the regression coefficients of a data analysis. 23.8 to >0 pts BASIC Interprets the statistical significance and effect size of the regression coefficients but the interpretation is incomplete, inaccurate, or logically inconsistent with the data. 0 pts NON_PERFORMANCE Does not interpret the statistical significance and effect size of the regression coefficients of a data analysis. |
/ 34 pts |
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Interpret the fit of the regression model for prediction of a data analysis. view longer description |
34 to >28.9 pts DISTINGUISHED Interprets the fit of the regression model of a data analysis for prediction and ensures the interpretation is complete, provides a perceptive and clearly articulated conclusion, and includes an assessment of caveats and limitations. 28.9 to >23.8 pts PROFICIENT Interprets the fit of the regression model for prediction of a data analysis. 23.8 to >0 pts BASIC Interprets the fit of the regression model for prediction of a data analysis but the interpretation is incomplete, inaccurate, or logically inconsistent with the data. 0 pts NON_PERFORMANCE Does not interpret the fit of the regression model for prediction of a data analysis. |
/ 34 pts |
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Apply the statistical results of the multiple regression of a data analysis to support a health care decision. view longer description |
34 to >28.9 pts DISTINGUISHED Applies the statistical results of the multiple regression of a data analysis to support a health care decision. Describes how results can help managers make a decision and provides significant details and justification for the decision. 28.9 to >23.8 pts PROFICIENT Applies the statistical results of the multiple regression of a data analysis to support a health care decision. 23.8 to >0 pts BASIC Applies the statistical results of the multiple regression of a data analysis to support a health care decision but work is incomplete, inaccurate, or logically inconsistent with the data. 0 pts NON_PERFORMANCE Does not apply the statistical results of the multiple regression of a data analysis to support a health care decision. |
/ 34 pts |
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Write a narrative summary that includes practical, administration-related implications of the multiple regression. view longer description |
32 to >27.2 pts DISTINGUISHED Writes a narrative summary that includes practical, administration-related implications of the multiple regression. Draws valid, fully justified conclusions well-supported by scholarly literature. 27.2 to >22.4 pts PROFICIENT Writes a narrative summary that includes practical, administration-related implications of the multiple regression. 22.4 to >0 pts BASIC Writes a narrative summary that contains incorrect or insufficient administration-related implications of the multiple regression. 0 pts NON_PERFORMANCE Does not write a narrative summary that includes practical, administration-related implications of the multiple regression. |
/ 32 pts |
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Write clearly and concisely, using correct grammar, mechanics, and APA formatting. view longer description |
32 to >27.2 pts DISTINGUISHED Writes clearly and concisely. Ensures grammar, mechanics, and APA formatting are error free. 27.2 to >22.4 pts PROFICIENT Write clearly and concisely, using correct grammar, mechanics, and APA formatting. 22.4 to >0 pts BASIC Writes in a manner that is unclear and disorganized, includes errors in grammar and mechanics that inhibit effective communication, or contains incorrect or improperly formatted source citations and references. 0 pts NON_PERFORMANCE Does not write clearly and concisely, using correct grammar, mechanics, and APA formatting. |
/ 32 pts |
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Total Points: 0 |
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