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HMGT 400 Research and Data Analysis in Health Care-Exercise Dataset: HMGTHOSP.csv (Please download dataset from the class). Required program: RStudio or R Programming. Author, Hossein

HMGT 400 Research and Data Analysis in Health Care-Exercise

Dataset: HMGTHOSP.csv (Please download dataset from the class).

Required program: RStudio or R Programming.

Author, Hossein Zare, PhD

Citation: Zare, H. (2017). HMGT 400 7980 Research and Data Analysis in Health Care-Exercise. UMGC.EDU

For each week exercise, For the RStudio codes and a video, please follow the class announcement page.

If you have chosen to work with Excel, please run the three linear regression models and complete the following tables.

Medicare and Medicaid Discharge Ratios:Medicare Discharges Total Hospital Discharges;Medicaid Discharges Total Hospital Discharges

Model 1:

Run a linear model to predict the impact of number of hospital beds (use bed-tot) on hospital net-benefit in teaching hospitals.

Hospital Characteristics Coef. ST. ERR T Stat P-values Lower 95% Upper 95%
Hospital beds
R Square

(Limit all results to 2 decimal places max)

Model 3:

Now, include theMedicare and Medicaid discharge ratios in first model. How do you evaluate the impact of having higher Medicare and Medicaid patients on hospital net-benefit in teaching hospitals?

Hospital Characteristics Coef. ST. ERR T Stat P-values Lower 95% Upper 95%
Hospital beds
Medicare-discharge-ratio
Medicaid-discharge-ratio
R Square

(Limit all results to 2 decimal places max)

Hospial net benefits total_hospital_beds Medicare discarge ratio Medicaid discarge raito own system_member teaching_hospital
3800000 507.79 0.24375172 0.295574147 1 0 1
26000000 50.47 0.269109308 0.312171838 0 0 1
1000000 91.67 0.329525862 0.415948276 0 0 1
-7000000 244.11 0.558573447 0.021437272 2 1 1
41000000 397.58 0.440587612 0.060718655 1 1 1
61000000 327.54 0.415043784 0.135848319 2 1 1
23000000 169.95 0.180636883 0.343489087 0 1 1
74000000 585.04 0.205662522 0.29287388 0 1 1
43000000 220.42 0.175532241 0.298999787 2 1 1
-5000000 126.69 0.461584906 0.284112661 2 0 1
10000000 403.76 0.229720432 0.140967031 0 0 1
-3000000 195.7 0.484640435 0.104163686 2 0 1
-3700000 412 0.167670129 0.343978226 1 0 1
10000000 198.79 0.182106185 0.247943596 0 0 1
2000000 204.97 0.276904949 0.125363932 2 0 1
21000000 176.13 0.165909753 0.097707424 2 0 1
1000000 71.07 0.300095195 0.176470588 0 0 1
3000000 359.47 0.417620373 0.142097663 0 0 1
24000000 110.21 0.52538068 0.131206741 1 0 1
380000000 893.01 0.373687233 0.069626919 0 0 1
15000000 507.79 0.30062257 0.110523301 2 0 1
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9500000 250.29 0.358266368 0.074208197 1 1 1
32000000 319.3 0.214201454 0.397871236 0 1 1
6600000 201.88 0.41354821 0.127675972 0 1 1
2000000 108.15 0.25785216 0.100487981 1 1 1
12000000 47.38 0.282820809 0.319845857 0 1 1
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16000000 49.44 0.229719659 0.122832795 1 1 1
95000000 209.09 0.267704916 0.116161319 0 1 1
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31000000 357.41 0.216139977 0.002499592 0 1 1
43000000 83.43 0.591669105 0.014631401 0 1 1
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276000000 361.53 0.447523402 0.005859815 1 1 1
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52000000 103 0.557660321 0.268756999 0 1 1
6000000 430.54 0.225336431 0.110273701 1 1 1
20300000 673.62 0.24873877 3.29337E-05 0 1 1
18000000 348.14 0.394121281 0.087068424 1 1 1
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80000000 449.08 0.381160537 4.10897E-05 1 1 1
470000000 816.79 0.20461114 0.436858316 0 1 1
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21100000 154.5 0.401776868 0.09825998 2 1 1
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9000000 55.62 0.340441091 0.184561811 0 1 1
100000000 151.41 0.298494287 0.135545469 0 1 1
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32000000 466.59 0.349833779 0.209470792 1 0 1
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30000000 501.61 0.095048178 0.10870499 0 1 1
61000000 55.62 0.292112538 0.120489297 0 1 1
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32000000 159.65 0.482225632 0.095837002 0 1 1
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7200000 91.67 0.545318317 0.190626035 2 0 1
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27900000 151.41 0.350999841 0.453216839 0 0 1
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2600000 64.89 0.45196319 0.428527607 2 1 1
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