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% Title: Nusring Home Data % Source: DASL - http://lib.stat.cmu.edu/DASL/Stories/nursinghome.html % Description: % The data were collected by the Department of Health and Social Services of the %...

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% Title: Nusring Home Data
% Source: DASL - http:
lib.stat.cmu.edu/DASL/Stories/nursinghome.html
% Description:
% The data were collected by the Department of Health and Social Services of the
% State of New Mexico and cover 52 of the 60 licensed nursing facilities in New Mexico
% in 1988.
%
% Number of cases: 52
% Variable Names:
% BED = number of beds in home
% MCDAYS = annual medical in-patient days (hundreds)
% TDAYS = annual total patient days (hundreds)
% PCREV = annual total patient care revenue ($hundreds)
% NSAL = annual nursing salaries ($hundreds)
% FEXP = annual facilities expenditures ($hundreds)
% RURAL = rural (1) and non-rural (0) homes
@relation NursingHome
@attribute BED numeric
@attribute MCDAYS numeric
@attribute TDAYS numeric
@attribute PCREV numeric
@attribute NSAL numeric
@attribute FEXP numeric
@attribute RURAL {0,1}

@data
244,128,385,23521,5230,5334,0
59,155,203,9160,2459,493,1
120,281,392,21900,6304,6115,0
120,291,419,22354,6590,6346,0
120,238,363,17421,5362,6225,0
65,180,234,10531,3622,449,1
120,306,372,22147,4406,4998,1
90,214,305,14025,4173,966,1
96,155,169,8812,1955,1260,0
120,133,188,11729,3224,6442,1
62,148,192,8896,2409,1236,0
120,274,426,20987,2066,3360,1
116,154,321,17655,5946,4231,0
59,120,164,7085,1925,1280,1
80,261,284,13089,4166,1123,1
120,338,375,21453,5257,5206,1
80,77,133,7790,1988,4443,1
100,204,318,18309,4156,4585,1
60,97,213,8872,1914,1675,1
110,178,280,17881,5173,5686,1
120,232,336,17004,4630,907,0
135,316,442,23829,7489,3351,0
59,163,191,9424,2051,1756,1
60,96,202,12474,3803,2123,0
25,74,83,4078,2008,4531,1
221,514,776,36029,1288,2543,1
64,91,214,8782,4729,4446,1
62,146,204,8951,2367,1064,0
108,255,366,17446,5933,2987,1
62,144,220,6164,2782,411,1
90,151,286,2853,4651,4197,0
146,100,375,21334,6857,1198,0
62,174,189,8082,2143,1209,1
30,54,88,3948,3025,137,1
79,213,278,11649,2905,1279,0
44,127,158,7850,1498,1273,1
120,208,423,29035,6236,3524,0
100,255,300,17532,3547,2561,1
49,110,177,8197,2810,3874,1
123,208,336,22555,6059,6402,1
82,114,136,8459,1995,1911,1
58,166,205,10412,2245,1122,1
110,228,323,16661,4029,3893,1
62,183,222,12406,2784,2212,1
86,62,200,11312,3720,2959,1
102,326,355,14499,3866,3006,1
135,157,471,24274,7485,1344,0
78,154,203,9327,3672,1242,1
83,224,390,12362,3995,1484,1
60,48,213,10644,2820,1154,0
54,119,144,7556,2088,245,1
120,217,327,20182,4432,6274,0
Answered Same Day Dec 02, 2020

Solution

Aakarsh answered on Dec 05 2020
139 Votes
Nursing Homes
Nursing Homes
Nursing Home Data Analysis
The data were collected by the Department of Health and Social Services of the State of New Mexico and cover 52 of the 60 licensed nursing facilities in New Mexico in 1988.
# Importing required packages
li
ary(
ew)
li
ary(RWeka)
li
ary(ggplot2)
li
ary(grid)
li
ary(ggplot2)
li
ary(GGally)
li
ary(reshape)
li
ary(gridExtra)
li
ary (cluster)
li
ary(outliers)
# Reading in dataframe
df<-read.arff("data.arff")
Overview of Data
head(df)
summary(df)
## BED MCDAYS TDAYS PCREV
## Min. : 25.00 Min. : 48.0 Min. : 83.0 Min. : 2853
## 1st Qu.: 62.00 1st Qu.:125.2 1st Qu.:198.0 1st Qu.: 8857
## Median : 88.00 Median :164.5 Median :279.0 Median :12384
## Mean : 93.27 Mean :183.9 Mean :280.2 Mean :14210
## 3rd Qu.:120.00 3rd Qu.:229.0 3rd Qu.:363.8 3rd Qu.:18777
## Max. :244.00 Max. :514.0 Max. :776.0 Max. :36029
## NSAL FEXP RURAL
## Min. :1288 Min. : 137 0:18
## 1st Qu.:2336 1st Qu.:1229 1:34
## Median :3696 Median :2378
## Mean :3813 Mean :2848
## 3rd Qu.:4840 3rd Qu.:4444
## Max. :7489 Max. :6442
Variable Names:
% BED = number of beds in home
% MCDAYS = annual medical in-patient days (hundreds)
% TDAYS = annual total patient days (hundreds)
% PCREV = annual total patient care revenue ($hundreds)
% NSAL = annual nursing salaries ($hundreds)
% FEXP = annual facilities expenditures ($hundreds)
% RURAL = rural (1) and non-rural (0) homes
Plots
Creating Histograms for all the 7 variables.
From Histrogram outliers can be seen easily in each of the 6 tuples. And data in highly non-uniform.
Lets see how different Nursing home attribute are related to each other using co
elation matrix.
#co
elation matrix
cormat<-cor(df[c("BED","MCDAYS","TDAYS","PCREV","NSAL","FEXP")])
cormat
## BED MCDAYS TDAYS PCREV NSAL FEXP
## BED 1.0000000 0.5680006 0.8182959 0.8437752 0.5094241 0.4602559
## MCDAYS 0.5680006 1.0000000 0.8116225 0.7070754 0.2541355 0.2583959
## TDAYS 0.8182959 0.8116225 1.0000000 0.9030608 0.5153965 0.3047354
## PCREV 0.8437752 0.7070754 0.9030608 1.0000000 0.5894065 0.4337859
## NSAL 0.5094241 0.2541355 0.5153965 0.5894065 1.0000000...
SOLUTION.PDF

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