MHA-FP5017 Assessment 3 Predicting an Outcome Using Regression Models

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
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

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

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.