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3540

year	hospital	pop	enrollment	RWPs	RVUs	prevscore	expenditures	education	admissions	dispositions	beddays	visits	ftes	satisfaction	access
																																															1																												2																																																			0			8	1			4			5	8.02				11			9			6	8			7		49			16				19	1	89																												3				14	672	80.			24	72.42
2	8960				22			18	9.19	2	29			15	87.79		64			23				20				21			10		52	25298	892	76.09	66.32
8	622	61690	23.32	4	33	86.81				13	5680	49	70	4980	1193	50789	1	598		75		63
105160	38020	108.18	744.37	92.07	3450900	9030	9000	4313	71479	4109	83.95	69.16
14	4150	89290	45.11	722.35	88.36	23	3250		10		340	2666	87409	2767		78.89	61.72
145210	67570	15.35	508.23	92.38	1606600	3270	745	59361	1410	78.				17	61.63
55890	55.24	452.5	90.34	2434900	5360	5330	2983	6			12	2967	81.92	7	3.43
6	492	43740	17.33	368.94	1178000	3800			3810	995	42202	1449	80.03	66.39
58610	26370	9.59	243.45		87.8	1085700	2050	2060	466	31963	1115	76.02	61.49
34690	24740	12.79		187	90.15	576500	2740	2	750	556	20	608	790	80.02	65.31
29120	11570	7.84		106	86.93	711900		1290	295			2150	856	79.14	65.85
3	5480	19610	13.26	303.51	93.55	880600	2450	2		470	931		3340	1	121	84.92	71.03
3	1120	13960	1.73	120.91	94.01	50		430		200	15384	607	83.59	69.03
138320	74410	82.74	782.34	2899200		10520	8	713	3617	85.49	74.79
76450	41	130	21.49	331.06	86.18	1136300		4430	1329		517	1483	77.92	68.95
55640	28580	194.63	88.8	662200	26288	773	78.92	65.88
58240	20710	7.68	248.55	91.32	860000		1320	34835	1010	82.82	70.85
52300	30		100	15.94	301.79	86.14		795		3230	872	35115	1175	80.42	64.92
137610	46190	99.19	698.13		89.6	3367100	13580	13550	5310	80156	3808	80.11	67.65
79830	19290	127.37	923.11	96.38	5917000	11700	1	1720	6713	77115	5616	80.76	61.93
14920	10830	4.52	68.87	86.09	3	505		1160	246	10201	560	79.51	63.58
5	8		930	34500	55.22	314.89	90.62	1701100	6980	6970			2720	37115	2	198	82.38	65.24
69690	49060	16.52	329.12	85.74	955300	4070	4080	1073	38148	1279	75.28	58.35
156460	9	1040	65.08	760.79	90.37	2402600	12620	12580	3390	89930	3012	78.19	61.07
22890	15130	7.1	121.78	87.08	495900			182	369	13745	694	72.23
28650	2		2480	7.69	201.43	84.78	659600	1860	1850	472	23713	904	76.91	69.79
85870	62260	22.83	426.03	83.67	1456000	5410	5390	1236	49350	1696	73.16	64.45
105680	33610	109.53	764.27	92.72	37	1650		8910	4435	72983	4344	74.69	67.1
142070	97690	43.57	700.47	85.03	2011600	10460	10440	2630	86975	2795	72.6	64.37
150340	73720	13.55	529.33	91.51	1738200	3110	3090	749	62483	1699	72.34	64.19
55930	56240	61.51	427.58	87.58	2397700	5660	2872	61084	2882	68.98	61.64
	632	44230	17.74	381.64	89.32	1212200	4200	4180		1060	44800	1552	72.07	66.28
58760	27990	11.33	254.39	90.26	1103700	2240	2230	32732	1128	73.8		65.72
		3380	26320	10.49	177.32	86.85	590600	496	20557	792	71.69
30090	15010	7.94	107.87	87.49	712200		1250	294	19714	845	70.19	63.46
33820	20810	13.39	271.78	90.36	893200			269	908	33461	76.21	72.95
31440		1419	1.27	104.07	95.02	500000	140	14282	70.69	66.67
138880	73560	90.05	781.01	89.43	3018200	11380	11350	4360	83896	3854	74.94	69.02
77370	43070	21.37	351.96	87.71	1224300		4390	1335	51644	1597	72.57	64.75
56830	28540	2.64	1	71.18	87.42	706600	25199	807	70.27	60.62
57510	21750	7.48	253.18	86.72	867600	1330	1340	33522	1041	75.54	72.59
50770	27830	353.46	89.1	8	1240	879	35808	1179	71.45	63.89
138780	47600	97.92	701.2	90.54	3444800	13370	13340	5153	80828		412	76.39
81970	20260	124.99	957.51	92.13	5519900	11030	10850	6883	81077	5901	71.66	66.92
14320	10430	3.87	62.33		84.6	378800	8475	569		73.19	67.3
58310	36440	55.64	337.2	89.37	1709200	6680	6640	2585	37779	2256		72.02	58.83
68670	49530	16.2	332.41	85.98	1037100	4130	4110	1035	39957	1404	73.52	65.11
159370	92590	780.5	87.2	2965900	12370	12340		3540	90718	3549	70.04	58.78
23670	14810	7.05	112.21	83.28	566600		1890	373	13233	70.55
27930	23090	6.51	182.38	83.4	726700	1660	25562	986	71.55	66.24
86670	68400	21.74	372.06	1639900	5340	5320	1261	51222	1766	63.02	57.29
106160	36370	106.51	604.1	4025100	4704	75013	4523	77.05	69.57
142830	105700	41.41	638.09	79.01	2068100	10250	10240	2465	86576	2921	72.13	64.07
154230	80620	14.14	476.48	89.14		1691	3360	60295	73.63	67.39
56070	56470	52.68	413.25	87.31	2553300	5490	2559	63841	2971	72.88		67.86
64180	49840	16.87	314.98	85.65	1254400	4000	4010	1001	42532	1525
58270	38130	10.41	229.08	84.82	1302300	34596	1304	69.87	65.61
33630	32870	10.74	79.7	672500	2540	2530	23214	868	70.83
30810	12740	7.07	85.1	531600	1230	13212	634	73.67	67.49
32730	23950	14.31	253.72	83.15	956000	922	35844	1173	74.81	70.59
32120	14930	0.96	76.53	89.44	523700	13584	642	69.85	65.4
140610	84060	88.25	751.23	84.44	3367000		11330	4463	88984	4289	71.68	66.84
7	554	47870	21.93	339.66	85.73	1291600	4830	4820	54313	74.35	69.3
58100	31500	3.01	153.56	82.32	719800	24016	881	69.81	60.92
55780	22990	6.71	252.2	85.63	996000		1180	35126	1113	80.99	74.52
50210	31390	14.87	298.59	83.97	925300	3190	3170	36410	1273	75.62	70.48
137800	48970	94.61	592.48	83.78	3792500	13300	13280	5220	78757	4228	74.51	72.43
83540	21880	120.82	928.24	91.74	5853400	10760	10710	7084	84567	6034	71.91	67.29
13760	10700	3.03	60.06	80.83	380800	8744	72.84	64.76
59950	40710	48.33	242.3	84.81	1894100	6040	6030	2324	37441	2452	71.85	57.58
67800	63400	14.86	327.31	80.24	1142900	1018	40577	1471	68.53	62.89
167510	103120	61.97	718.52	82.12	3179800	11810	11800	90870	3885	68.85	63.07

Find the attached CSV file that contains yearly information about hospitals.

 

The following variable definitions apply.

 

1)  year:  year in which the observation was made with 1 = 2001, 2=2002, 3= 2003

2)  hospital:  hospital id, from 1 to 24

3)  pop:  potential population supported in the community

4)  enrollment:  actual enrolled individuals from the community (cannot exceed population)

5)  RWPs:  inpatient weighted workload (standardized workload)

6)  RVUs:  outpatient weighted workload (standardized workload)

7)  prevscore:  prevention score (0 to 100), percent of 10 metrics achieved, empirical

8)  expenditures:  how much money was spent by this facility during the year

9)  education:  whether or not a facility provides graduate medical education, {0,1}

10) admissions:  number of admissions during the year

11)  dispositions:  number of dispositions during the year

12)  beddays: number of bed days during the year

13) visits:  number of outpatient visits during the year

14)  ftes:  number of full-time employees per year

15)  satisfaction:  overall patient satisfaction, percent by year

16)  access:  satisfaction with access, percent by year

 

1)  Import the dataset into R.  Identify whether each variable is quantitative or qualitative.  Identify  the level of measurement of each variable. 

2)  Provide appropriate descriptives statistics for all variables.  Be sure to provide measures of center and measures of dispersion. 

3) Provide graphs of all variables  (boxplots, histograms, bar charts, etc.) as appropriate.  DO NOT provide qualitative graphs for quantitative data and vice versa.

4)  Test the hypothesis that mean patient satisfaction (15) and access (16) are correlated.  Provide a scatterplot of the two.  What might this mean?

5)  Test the hypothesis that mean prevention scores (7) differ based on whether GME is present (9)

6)  Test the hypothesis that mean expenditures (8) for all facilities are identical for all three years (1).  Run appropriate post-hoc tests.  Interpret all tests.

7)  Test whether expenditures (8) is normally distributed. Provide a complete interpretation of the tests.  If it is not, find the best transformation and add the transformed variable to your dataset (17, if needed).

8)  Using expenditures (8) or your transformed variable (17), build a model of expenditures that includes all quantitative variables.  Test for collinearity of variables.  If you have collinearity, remove one of the offending variables and re-run the analysis.  Interpret all output. Provide examples of how this model might be used to forecast expenditures for a facility within this three year period.