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Assignment 3- Predicting an Outcome Using Regression Models Overview 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. Note: You are strongly encouraged to complete the assessments in this course in the order they are presented. 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 assessment, you will perform multiple regression and generate a prediction to support a health care decision. By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria: Competency 2: Analyze data using computer-based programming and software.Perform the appropriate multiple regression using a dataset.Competency 3: Interpret results of data analysis for value-based health care decisions, policy, or practice.Interpret the statistical significance and effect size of the regression coefficients of a data analysis.Interpret the fit of the regression model for prediction of a data analysis.Competency 4: Present results of data analysis to support a decision or recommendation.Apply the statistical results of the multiple regression of a data analysis to support a health care decision.Write a narrative summary of the results that includes practical, administration-related implications of the multiple regression.Competency 5: Communicate audience-appropriate health management content in a logically structured and concise manner, writing clearly with correct use of grammar, punctuation, spelling, and APA style.Write clearly and concisely, using correct grammar, mechanics, and APA formatting. Resources Multiple Linear Regression Casson, R. J., & Farmer, L. D. M. (2014). Understanding and checking the assumptions of linear regression: A primer for medical researchers. Clinical & Experimental Ophthalmology, 42(6), 590–596.Frey, B. B. (Ed.). (2018). Multiple linear regression. In The SAGE encyclopedia of educational research, measurement, and evaluation (Vols. 1–4). Thousand Oaks, CA: Sage.Katz, M. H. (2003). Multivariable analysis: A primer for readers of medical research. Annals of Internal Medicine, 138(8), 644–650.Multiple Regression in Microsoft Excel.Statsoft.com. (n.d.). How to find relationship between variables, multiple regression. Retrieved from http://www.statsoft.com/Textbook/Multiple-Regressi... Regression Analysis Gallo, A. (2015, November 04). A refresher on regression analysis. Harvard Business Review Digital Articles, 2–9.SCSUEcon. (2011, August 20). Linear regression in Excel [Video] | Transcript. Retrieved from Using Regression Analysis Every Day. Effect Size Sullivan, G. M. (2012). FAQs about effect size. Journal of Graduate Medical Education, 4(3), 283–284. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC34441...Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. Predictive Analytics Davenport, T. H. (2014, September 02). A predictive analytics primer. Harvard Business Review Digital Articles, 2–4.IntroToIS BYU. (2016, November 04). Creating a multiple linear regression predictive model in Excel [Video] | Transcript. Retrieved from Textbook Kros, J. F., & Rosenthal, D. A. (2016). Statistics for health care management and administration: Working with Excel (3rd. ed.). San Francisco, CA: Jossey-Bass. Available in the courseroom via the VitalSource Bookshelf link. Assessment Instructions Preparation Download the Assessment 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) . Instructions Hospital administration needs to make a decision on the amount of reimbursement required to cover expected costs for next year. For this assessment, using 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 Copy From Excel to Another Office Program for instructions. Submit both the Word document and the Excel file that shows the results. Grading Criteria The numbered assessment instructions outlined below correspond to the grading criteria in the 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. Perform the appropriate multiple regression using a dataset.Interpret the statistical significance and effect size of the regression coefficients of a data analysis.Interpret p-value and beta values.Interpret the fit of the regression model for prediction of a data analysis.Interpret R-squared and goodness of fit.Apply the statistical results of the multiple regression of a data analysis to support a health care decision.Generate a prediction with regression equation.Write a narrative summary of the results that includes practical, administration-related implications of the multiple regression.Write clearly and concisely, using correct grammar, mechanics, and APA formatting. Additional Requirements Your assessment should meet the following requirements: Written communication: Write clearly, accurately, and professionally, incorporating sources appropriately.Length: 3–4 pagesResources: Not required.APA format: Cite your sources using current APA format.Font and font size: Times Roman, 10 point. data_analysis_health_care_decisions_assignment_3_details_and_instructions.docx data_analysis_health_care_decisions_assignment_3_dataset.xlsx data_analysis_health_care_decisions_assignment_3_grading_rubrics.pdf Unformatted Attachment Preview Assignment 3- Predicting an Outcome Using Regression Models Overview 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. Note: You are strongly encouraged to complete the assessments in this course in the order they are presented. 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 assessment, you will perform multiple regression and generate a prediction to support a health care decision. By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria: • o • o o • o o • o Competency 2: Analyze data using computer-based programming and software. Perform the appropriate multiple regression using a dataset. Competency 3: Interpret results of data analysis for value-based health care decisions, policy, or practice. Interpret the statistical significance and effect size of the regression coefficients of a data analysis. Interpret the fit of the regression model for prediction of a data analysis. Competency 4: Present results of data analysis to support a decision or recommendation. Apply the statistical results of the multiple regression of a data analysis to support a health care decision. Write a narrative summary of the results that includes practical, administrationrelated implications of the multiple regression. Competency 5: Communicate audience-appropriate health management content in a logically structured and concise manner, writing clearly with correct use of grammar, punctuation, spelling, and APA style. Write clearly and concisely, using correct grammar, mechanics, and APA formatting. Resources Multiple Linear Regression • • • • • Casson, R. J., & Farmer, L. D. M. (2014). Understanding and checking the assumptions of linear regression: A primer for medical researchers. Clinical & Experimental Ophthalmology, 42(6), 590–596. Frey, B. B. (Ed.). (2018). Multiple linear regression. In The SAGE encyclopedia of educational research, measurement, and evaluation (Vols. 1–4). Thousand Oaks, CA: Sage. Katz, M. H. (2003). Multivariable analysis: A primer for readers of medical research. Annals of Internal Medicine, 138(8), 644–650. Multiple Regression in Microsoft Excel. Statsoft.com. (n.d.). How to find relationship between variables, multiple regression. Retrieved from http://www.statsoft.com/Textbook/MultipleRegression Regression Analysis • • • Gallo, A. (2015, November 04). A refresher on regression analysis. Harvard Business Review Digital Articles, 2–9. SCSUEcon. (2011, August 20). Linear regression in Excel [Video] | Transcript. Retrieved from https://www.youtube.com/watch?v=TkiB1xBnjn4 Using Regression Analysis Every Day. Effect Size • • Sullivan, G. M. (2012). FAQs about effect size. Journal of Graduate Medical Education, 4(3), 283–284. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444175/ Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. Predictive Analytics • • Davenport, T. H. (2014, September 02). A predictive analytics primer. Harvard Business Review Digital Articles, 2–4. IntroToIS BYU. (2016, November 04). Creating a multiple linear regression predictive model in Excel [Video] | Transcript. Retrieved from https://www.youtube.com/watch?v=rWJKLqp3wWs Textbook • Kros, J. F., & Rosenthal, D. A. (2016). Statistics for health care management and administration: Working with Excel (3rd. ed.). San Francisco, CA: JosseyBass. Available in the courseroom via the VitalSource Bookshelf link. Assessment Instructions Preparation Download the Assessment 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). Instructions Hospital administration needs to make a decision on the amount of reimbursement required to cover expected costs for next year. For this assessment, using 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 Copy From Excel to Another Office Program for instructions. Submit both the Word document and the Excel file that shows the results. Grading Criteria The numbered assessment instructions outlined below correspond to the grading criteria in the Predicting an Outcome Using Regression Models Scoring Guide, so be sure to address each point. You may also want to review the performancelevel 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 a data analysis. o Interpret p-value and beta values. 3. Interpret the fit of the regression model for prediction of a data analysis. o Interpret R-squared and goodness of fit. 4. Apply the statistical results of the multiple regression of a data analysis to support a health care decision. o Generate a prediction with regression equation. 5. Write a narrative summary of the results that includes practical, administrationrelated implications of the multiple regression. 6. Write clearly and concisely, using correct grammar, mechanics, and APA formatting. Additional Requirements Your assessment should meet the following requirements: • • • • • Written communication: Write clearly, accurately, and professionally, incorporating sources appropriately. Length: 3–4 pages Resources: Not required. APA format: Cite your sources using current APA format. Font and font size: Times Roman, 10 point. cost age 13316 14043 17354 15338 14510 14703 17422 16958 12873 12185 10096 13787 29478 15900 16030 16037 11163 15974 15055 18636 9063 18247 14216 14881 17133 13668 15142 10834 10321 13906 18759 12974 17186 17262 17030 15655 16662 10036 16471 16269 17714 15179 29408 16060 15980 12265 risk 83 72 64 81 77 81 64 69 82 68 64 71 73 74 67 78 72 78 75 84 61 65 72 75 80 82 76 66 70 81 77 69 81 63 76 68 79 62 69 79 65 68 90 63 78 68 6 9 8 5 4 10 9 4 9 2 4 5 9 9 7 2 7 6 7 6 5 3 6 1 8 2 2 2 9 1 9 10 7 4 7 4 3 6 10 5 8 7 10 6 4 3 satisfaction 61 20 40 22 38 63 33 3 66 47 20 94 5 16 77 78 99 68 58 83 92 23 68 6 70 4 66 93 71 14 68 77 41 38 59 6 32 50 45 23 16 32 96 35 13 55 14254 15594 13452 12095 14817 18176 14847 14624 18471 13785 13380 14227 13824 11915 18384 16575 15453 13527 16285 15307 14514 12579 14772 15122 15473 15719 15123 16091 14874 14679 13380 17630 16704 13428 12101 13571 13117 12183 14181 15484 14478 16057 15577 14588 16739 15358 13913 80 74 73 67 75 68 69 74 83 83 70 73 73 81 62 79 64 63 64 76 75 68 74 63 75 82 76 78 67 74 74 82 80 70 83 71 70 77 82 76 74 78 63 74 80 82 65 5 8 8 6 7 5 9 1 8 7 10 5 1 3 4 10 1 1 5 7 7 2 2 5 1 6 8 7 1 2 2 6 4 8 4 2 5 6 3 5 2 9 5 6 8 10 4 3 60 12 93 92 35 1 63 76 77 33 84 6 26 3 54 68 45 17 71 81 8 21 1 35 84 73 38 40 88 69 48 65 42 26 21 88 7 25 29 19 13 91 45 90 19 15 14125 12880 15720 16391 15230 15972 16008 15702 13049 10734 18683 18203 14154 14263 14163 12866 12349 14472 11520 14927 12136 13981 11401 12852 13749 13861 14603 14336 13335 14400 15754 16600 12758 14797 14520 12959 17617 12974 14981 15989 14886 11331 15457 14366 14083 14247 15539 84 69 63 77 82 67 78 67 87 82 86 78 61 73 72 69 68 73 66 75 67 70 73 69 71 67 74 62 70 73 77 63 69 82 74 69 81 69 75 78 75 65 77 73 72 73 82 4 8 4 8 9 5 6 3 9 7 8 5 2 10 9 3 1 4 2 5 1 10 3 4 6 4 9 8 4 6 9 6 8 6 2 9 8 9 3 7 1 7 1 6 4 1 4 74 13 16 41 48 16 10 61 48 53 56 96 5 82 59 59 70 36 44 38 49 11 77 96 31 96 17 2 53 62 11 58 4 56 88 57 89 25 19 86 69 68 24 61 83 77 83 15190 28764 10596 13844 14636 14887 14232 14508 12702 16195 13793 12184 15796 12607 13368 14787 16267 17924 12524 16437 14610 15227 15197 13945 13461 13588 14413 15600 13969 15903 13549 15694 17127 15257 16370 14542 16317 16846 14063 13904 12988 16284 15128 11164 16788 74 86 77 71 63 82 73 62 81 78 77 67 74 68 83 76 61 82 81 79 69 76 75 72 70 63 74 77 72 74 71 77 67 76 67 62 79 80 72 71 70 79 76 66 70 7 6 4 9 5 5 2 4 6 4 10 6 2 2 9 6 9 10 2 8 8 6 9 6 8 3 9 10 10 7 2 2 4 1 6 8 10 7 8 8 8 9 1 8 10 42 28 79 85 44 99 96 44 88 12 29 76 88 41 5 1 95 97 91 23 69 73 12 81 44 80 44 42 40 70 19 52 75 37 78 27 83 51 63 56 47 51 50 96 90 12/31/2019 Predicting an Outcome Using Regression Models Scoring Guide Predicting an Outcome Using Regression Models Scoring Guide CRITERIA NONPERFORMANCE BASIC PROFICIENT DISTINGUISHED Perform the appropriate multiple regression using a dataset. Does not perform the appropriate multiple regression using a dataset. Incorrectly performs the appropriate multiple regression using a dataset. Performs the appropriate multiple regression using a dataset. Perform the appropriate multiple regression using a dataset and provides concise, logical reasoning for the multiple regression. Interpret the statistical significance and effect size of the regression coefficients of a data analysis. Does not interpret the statistical significance and effect size of the regression coefficients of a data analysis. Interprets the statistical significance and effect size of the regression coefficients of a data analysis but the interpretation is incomplete, inaccurate, or logically inconsistent with the data. Interprets the statistical significance and effect size of the regression coefficients of a data analysis. 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. Interpret the fit of the regression model for prediction of a data analysis. Does not interpret the fit of the regression model for prediction of a data analysis. 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. Interprets the fit of the regression model for prediction of a data analysis. Interprets the fit of the regression model for prediction 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. Apply the statistical results of the multiple regression of a data analysis to support a health care decision. Does not apply the statistical results of the multiple regression of a data analysis to support a health care decision. 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. Applies the statistical results of the multiple regression of a data analysis to support a health care decision. 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. Write a narrative summary of the results that includes practical, administrationrelated implications of the multiple regression. Does not write a scholarly narrative summary of the results that includes practical, administrationrelated implications of the multiple regression. Writes a narrative summary of the results that contains incorrect or insufficient administration-related implications of the multiple regression. Writes a narrative summary of the results that includes practical, administrationrelated implications of the multiple regression. Writes a scholarly narrative summary of the results that includes practical, administration-related implications of the multiple regression. Draws valid, fully justified conclusions well supported by scholarly literature. https://courserooma.capella.edu/bbcswebdav/institution/MHA-FP/MHA-FP5017/190700/Scoring_Guides/a03_scoring_guide.html 1/2 12/31/2019 CRITERIA Write clearly and concisely, using correct grammar, mechanics, and APA formatting. Predicting an Outcome Using Regression Models Scoring Guide NONPERFORMANCE Does not write clearly and concisely, using correct grammar, mechanics, and APA formatting. BASIC PROFICIENT DISTINGUISHED 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. Writes clearly and concisely, using correct grammar, mechanics, and APA formatting. Writes clearly and concisely. Ensures grammar, mechanics, and APA formatting are error free. https://courserooma.capella.edu/bbcswebdav/institution/MHA-FP/MHA-FP5017/190700/Scoring_Guides/a03_scoring_guide.html 2/2 ... Purchase answer to see full attachment