Aim: To analyze performance.

“Oct 13”, “Nov 13”, “Dec 13”, “Jan 14”, “Feb 14”, “Mar 14”, “Apr 14”, “May 14”, “Jun 14”, “Jul 14”, “Aug 14”, “Sept 14”

##    Month Received Resolved Reopen
## 1      1      104      105      5
## 2      2      105       99      2
## 3      3       84       69      1
## 4      4       80       66      2
## 5      5      122      115      4
## 6      6      157      153      3
## 7      7      162      157      1
## 8      8      130      122      3
## 9      9      139      134      5
## 10    10      151      145      4
## 11    11      118      131      3
## 12    12      109      108      2

Plotting graph:

g1 <- ggplot(ind1, aes(x=Month, y=NumberOfCases))
g1 <- g1 + geom_line(aes(y=Received), colour="purple")
g1 <- g1 + geom_line(aes(y=Resolved), colour="maroon")
g1 <- g1 + geom_line(aes(y=Reopen), colour="red")
g1

plot of chunk unnamed-chunk-2

In order to evaluate performance, it requires number of cases resolved on time, average first response time and average resolution time, which is currently not available.

Load Analysis

load<- data.frame(k=c(1,2,3,4,5,6,7,8,9,10,11,12), x=c(104, 105, 84, 80, 122, 157, 162, 130, 139, 151, 118, 109), y=c(105, 99, 69, 66, 115, 153, 157, 122, 134, 145, 131, 108), i= c(0,6,15,14,7,4,5,8,5,6,0,1))
nc2<-c("Month","Received","Resolved","Backlog")
nr2<-c(1,2,3,4,5,6,7,8,9,10,11,12)
colnames(load)<-nc2
rownames(load)<-nr2
load
##    Month Received Resolved Backlog
## 1      1      104      105       0
## 2      2      105       99       6
## 3      3       84       69      15
## 4      4       80       66      14
## 5      5      122      115       7
## 6      6      157      153       4
## 7      7      162      157       5
## 8      8      130      122       8
## 9      9      139      134       5
## 10    10      151      145       6
## 11    11      118      131       0
## 12    12      109      108       1

Plotting graph:

gL1 <- ggplot(load, aes(x=Month, y=NumberOfCases)) + geom_line(aes(y=Received, colour="Received"))+labs(title="Load Analysis") + geom_line(aes(y=Resolved, colour="Resolved")) + geom_line(aes(y=Backlog, colour="Backlog")) + scale_colour_manual("", breaks=c("Received","Resolved","Backlog"), values=c("purple","maroon","orange"))
gL1

plot of chunk unnamed-chunk-4

Predicting performance for the next month

For Received cases:

library(lattice)
library(caret)
meanReceived <- mean(ind1$Received)
stdev <- sd (ind1$Received)
n <- 12
meanReceived + c(-1,1) * qt(.975, n-1) * stdev / sqrt(n)
## [1] 104.6 138.9

Therefore, there is a 95% chance that the number of Received cases for the next month will be between 104.6 and 138.9.

For Resolved cases:
meanResolved <- mean(ind1$Resolved)
stdevRes <- sd (ind1$Resolved)
meanResolved + c(-1,1) * qt(.975, n-1) * stdevRes / sqrt(n)
## [1]  98.18 135.82

Therefore, there is a 95% chance that the number of Resolved cases for the next month will be between 98.18 and 135.82.

For Resolved cases:
meanReopen <- mean(ind1$Reopen)
stdevReo <- sd (ind1$Reopen)
meanReopen + c(-1,1) * qt(.975, n-1) * stdevReo / sqrt(n)
## [1] 2.041 3.793

Therefore, there is a 95% chance that the number of Reopened cases for the next month will be between 2.041 and 3.793.