Homework 6
Kymmi
12/1/2017
I am doing this analysis to see if there is a correlation between In State Tuition and the Average ACT Score of accepted students.
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writeeduc <- read.csv("/Users/kimberlyhatlestad/Data Mining/Educ4HW.csv")
head(educ)## College State Public MathSAT VerbSAT ACT Received
## 1 Christendom College VA 0 568 568 23 81
## 2 Mount Vernon College DC 0 440 402 20 149
## 3 King's College NY 0 442 465 25 356
## 4 Alaska Pacific University AK 0 490 482 20 193
## 5 Sierra Nevada College NV 0 400 400 21 200
## 6 Trinity College DC 0 470 480 22 247
## Accepted Enrolled Pct10 Pct25 FTUG PTUG ISTuit OSTuit Books PhDs SFRatio
## 1 72 51 33 71 139 3 8730 8730 400 92 9.3
## 2 70 61 15 35 203 138 13780 13780 500 84 5.7
## 3 233 53 16 24 246 18 8190 8190 500 100 7.0
## 4 146 55 16 44 249 869 7560 7560 800 76 11.9
## 5 160 100 5 25 300 50 7500 7500 500 45 7.5
## 6 189 100 19 49 309 639 11412 11412 500 89 8.3Getting rid of alphanumeric variables State and School Name.
educ <- educ[,-2]
head(educ)## College Public MathSAT VerbSAT ACT Received Accepted
## 1 Christendom College 0 568 568 23 81 72
## 2 Mount Vernon College 0 440 402 20 149 70
## 3 King's College 0 442 465 25 356 233
## 4 Alaska Pacific University 0 490 482 20 193 146
## 5 Sierra Nevada College 0 400 400 21 200 160
## 6 Trinity College 0 470 480 22 247 189
## Enrolled Pct10 Pct25 FTUG PTUG ISTuit OSTuit Books PhDs SFRatio
## 1 51 33 71 139 3 8730 8730 400 92 9.3
## 2 61 15 35 203 138 13780 13780 500 84 5.7
## 3 53 16 24 246 18 8190 8190 500 100 7.0
## 4 55 16 44 249 869 7560 7560 800 76 11.9
## 5 100 5 25 300 50 7500 7500 500 45 7.5
## 6 100 19 49 309 639 11412 11412 500 89 8.3set.seed(1)
grpeduc<-kmeans(educ[,-1],centers=8,nstart=10)
Cluseduc<-cbind(educ,cluster=grpeduc$cluster)
head(Cluseduc)## College Public MathSAT VerbSAT ACT Received Accepted
## 1 Christendom College 0 568 568 23 81 72
## 2 Mount Vernon College 0 440 402 20 149 70
## 3 King's College 0 442 465 25 356 233
## 4 Alaska Pacific University 0 490 482 20 193 146
## 5 Sierra Nevada College 0 400 400 21 200 160
## 6 Trinity College 0 470 480 22 247 189
## Enrolled Pct10 Pct25 FTUG PTUG ISTuit OSTuit Books PhDs SFRatio cluster
## 1 51 33 71 139 3 8730 8730 400 92 9.3 8
## 2 61 15 35 203 138 13780 13780 500 84 5.7 3
## 3 53 16 24 246 18 8190 8190 500 100 7.0 8
## 4 55 16 44 249 869 7560 7560 800 76 11.9 8
## 5 100 5 25 300 50 7500 7500 500 45 7.5 8
## 6 100 19 49 309 639 11412 11412 500 89 8.3 3o=order(grpeduc$cluster)
data.frame(educ$ISTuit[o],grpeduc$cluster[o])## educ.ISTuit.o. grpeduc.cluster.o.
## 1 18800 1
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## 385 7800 8Creating the plot to show the different clusters.
summary(educ$ISTuit)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 608 4103 9600 9187 12820 24940plot(educ$ISTuit,educ$ACT,type="n",xlim=c(3000,18000),xlab="In State Tuition",ylab="Average ACT Score")
text(x=educ$ISTuit,y=educ$ACT,labels=educ$College,col=rainbow(8)[grpeduc$cluster])
Here you can see a slight positive correlation between In State Tuition and Average ACT scores for accepted students. I would like to have a more spread out plot to clearer see the separations between the clusters, seeing as how this plot shows a lot of overlap between the two groups.