Data Analysis Student Results

Exercise Week 5 + 6

Loading the Data

dfStudent <- read.table(file="clipboard", header=T)
dfStudent

##    student gender X2nd age a1 a2 a3 a4 a5 totala hw2 hw3 hw4 hw5 totalhw
## 1   gerrit      m  MBO  21  1  1  1  1  1      5   1   1   1   1       4
## 2     saar      f HAVO  23  1  1  1  1  1      5   1   0   1   1       3
## 3     lars      m  VWO  19  1  1  1  1  1      5   1   1   1   1       4
## 4     henk      m  MBO  23  1  1  1  1  1      5   1   1   1   1       4
## 5    klara      f  MBO  19  1  0  1  0  1      3   0   1   0   1       2
## 6      sem      m HAVO  20  1  1  1  1  1      5   1   1   1   1       4
## 7      liv      f HAVO  19  1  1  1  1  1      5   1   1   1   1       4
## 8    rinus      m  VWO  19  1  1  1  0  1      4   1   1   0   1       3
## 9     tess      f  MBO  19  0  1  1  0  0      2   1   1   0   0       2
## 10     tim      m HAVO  18  1  1  1  1  1      5   1   0   1   1       3
## 11    lisa      f HAVO  19  1  1  1  1  1      5   1   1   1   1       4
## 12   lotte      f HAVO  24  1  1  1  1  1      5   1   1   1   1       4
## 13   tinus      m  MBO  26  1  0  1  0  1      3   1   0   0   0       1
## 14   karel      m  MBO  20  1  1  1  0  1      4   1   1   1   1       4
## 15    mila      f  VWO  19  1  1  1  1  1      5   1   1   1   1       4
## 16 betrand      m HAVO  21  1  1  1  1  1      5   1   1   1   1       4
## 17    liam      m  MBO  19  1  1  1  0  1      4   1   1   0   1       3
## 18   janus      m  MBO  18  1  1  1  1  1      5   1   1   1   1       4
## 19    riek      f HAVO  19  1  0  0  1  1      3   1   0   0   1       2
## 20     zus      f HAVO  20  1  0  1  1  1      4   1   1   0   1       3
## 21  jayden      m HAVO  19  1  1  1  0  1      4   1   1   0   1       3
## 22     ans      f HAVO  22  1  1  1  1  1      5   1   1   1   1       4
## 23    sien      f  MBO  21  1  1  1  1  0      4   1   1   1   0       3
## 24  thomas      m  VWO  20  1  1  1  1  1      5   1   1   1   1       4
## 25    loek      m HAVO  24  1  1  1  0  1      4   1   1   1   0       3
## 26    daan      m  VWO  20  1  1  0  0  1      3   1   0   0   0       1
## 27    bert      m HAVO  19  1  1  1  1  1      5   1   1   1   1       4
## 28    noah      m HAVO  21  1  1  1  0  1      4   1   0   0   1       2
## 29   thijs      m  VWO  20  1  1  1  1  1      5   1   1   1   1       4
## 30   jesse      m HAVO  18  1  0  1  1  1      4   1   0   1   1       3
## 31   julia      f  VWO  20  1  1  0  1  1      4   1   1   1   1       4
## 32    piet      m  MBO  20  1  1  1  0  1      4   1   1   1   1       4
## 33     jet      f  MBO  21  1  1  1  1  1      5   1   1   1   1       4
## 34    bram      m  MBO  21  1  1  0  1  0      3   1   0   1   0       2
## 35     ans      f HAVO  25  1  1  0  0  1      3   1   1   0   0       2
## 36     eva      f HAVO  20  1  1  1  1  0      4   1   1   1   0       3
## 37    mees      m  MBO  24  1  1  1  1  1      5   1   1   1   1       4
## 38    levi      m HAVO  21  1  0  1  0  1      3   1   0   1   0       2
## 39    luuk      m  MBO  20  1  1  1  1  1      5   1   1   1   1       4
## 40     zoë      f  MBO  19  1  1  1  1  1      5   1   1   1   1       4
## 41   sjaak      m HAVO  20  1  0  1  1  1      4   1   1   1   1       4
## 42    theo      m  MBO  21  1  1  1  1  1      5   1   1   0   1       3
## 43   lucas      m HAVO  21  1  1  0  1  1      4   1   1   0   1       3
## 44  nellie      f  VWO  22  1  0  1  0  1      3   1   1   0   1       3
##    exam USG
## 1     7   S
## 2     6   S
## 3     8   G
## 4     9   G
## 5     6   S
## 6     7   S
## 7     8   G
## 8     9   G
## 9     4   U
## 10    9   G
## 11    8   G
## 12    8   G
## 13    3   U
## 14    4   U
## 15    8   G
## 16    5   U
## 17    6   S
## 18    7   S
## 19    5   U
## 20    6   S
## 21    4   U
## 22    8   G
## 23    6   S
## 24    9   G
## 25    5   U
## 26    2   U
## 27    7   S
## 28    5   U
## 29    9   G
## 30    5   U
## 31    8   G
## 32    5   U
## 33    4   U
## 34    4   U
## 35    3   U
## 36    7   S
## 37    6   S
## 38    4   U
## 39    8   G
## 40    9   G
## 41    6   S
## 42    4   U
## 43    6   S
## 44    5   U

1. Frequency distribution for

Gender

table(dfStudent$gender)

## 
##  f  m 
## 17 27

Secondary School

table(dfStudent$X2nd)

## 
## HAVO  MBO  VWO 
##   20   16    8

Age Class (17-<20; 20-<23; 23-<)

# 17-20
age <- dfStudent$age
range(age)

## [1] 18 26

break1<- seq(17,20,by=1)
break1

## [1] 17 18 19 20

age1.cut = cut(age, break1, right=FALSE) 
age1.freq <- table(age1.cut)
age1.freq

## age1.cut
## [17,18) [18,19) [19,20) 
##       0       3      12

#20-23
break2 <- seq(20,23,by=1)
break2

## [1] 20 21 22 23

age2.cut <- cut(age,break2,right = FALSE)
age2.freq <-table (age2.cut)
age2.freq

## age2.cut
## [20,21) [21,22) [22,23) 
##      11       9       2

#23<
break3 <- seq(23,26, by=1)
break3

## [1] 23 24 25 26

age3.cut = cut(age,break3, right=FALSE)
age3.freq = table(age3.cut)
age3.freq

## age3.cut
## [23,24) [24,25) [25,26) 
##       2       3       1

Attendance (0-<2; 2-<4; 4; 5)

#0-<2
dfStudent$totala

##  [1] 5 5 5 5 3 5 5 4 2 5 5 5 3 4 5 5 4 5 3 4 4 5 4 5 4 3 5 4 5 4 4 4 5 3 3
## [36] 4 5 3 5 5 4 5 4 3

attendance1 <- seq(0,2, by=1)
attendance1.cut <- cut(dfStudent$totala, attendance1, right = FALSE)
attendance1.freq <- table (attendance1.cut)
attendance1.freq

## attendance1.cut
## [0,1) [1,2) 
##     0     0

#2-<4
attendance2 <- seq(2,4, by=1)
attendance2.cut <- cut(dfStudent$totala, attendance2, right = FALSE)
attendance2.freq <- table (attendance2.cut)
attendance2.freq

## attendance2.cut
## [2,3) [3,4) 
##     1     8

#4
attendance3 <- seq(4,5, by=1)
attendance3.cut <- cut(dfStudent$totala, attendance3, right = FALSE)
attendance3.freq <- table (attendance3.cut)
attendance3.freq

## attendance3.cut
## [4,5) 
##    14

#5
attendance4 <- seq(5,6, by=1)
attendance4.cut <- cut(dfStudent$totala, attendance4, right = FALSE)
attendance4.freq <- table (attendance4.cut)
attendance4.freq

## attendance4.cut
## [5,6) 
##    21

Homework Submitted (0-<2; 2-<4; 4; 5)

#0-<2
hw <- dfStudent$totalhw
hw1 <- seq(0,2, by=1)
hw1.cut <- cut(dfStudent$totalhw,hw1,righ=FALSE)
hw1.freq <- table (hw1.cut)
hw1.freq

## hw1.cut
## [0,1) [1,2) 
##     0     2

#2-<4
hw2 <- seq(2,4, by=1)
hw2.cut <- cut(dfStudent$totalhw,hw2,righ=FALSE)
hw2.freq <- table (hw2.cut)
hw2.freq

## hw2.cut
## [2,3) [3,4) 
##     7    13

#4
hw3 <- seq(4,5, by=1)
hw3.cut <- cut(dfStudent$totalhw,hw3,righ=FALSE)
hw3.freq <- table (hw3.cut)
hw3.freq

## hw3.cut
## [4,5) 
##    22

#5
hw4 <- seq(5,6, by=1)
hw4.cut <- cut(dfStudent$totalhw,hw4,righ=FALSE)
hw4.freq <- table (hw4.cut)
hw4.freq

## hw4.cut
## [5,6) 
##     0

2. central tendencies for

Results

summary(dfStudent$exam)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.000   5.000   6.000   6.182   8.000   9.000

mean(dfStudent$exam)

## [1] 6.181818

median(dfStudent$exam)

## [1] 6

mode(dfStudent$exam)

## [1] "numeric"

Number of attendance

summary(dfStudent$totala)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2.00    4.00    4.00    4.25    5.00    5.00

mean(dfStudent$totala)

## [1] 4.25

median(dfStudent$totala)

## [1] 4

mode(dfStudent$totala)

## [1] "numeric"

Number of homework submitted

summary(dfStudent$totalhw)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00    3.00    3.50    3.25    4.00    4.00

mean(dfStudent$totalhw)

## [1] 3.25

median(dfStudent$totalhw)

## [1] 3.5

mode(dfStudent$totalhw)

## [1] "numeric"

3.data dispersity for

Results

var(dfStudent$exam)

## [1] 3.687104

IQR(dfStudent$exam)

## [1] 3

sd(dfStudent$exam)

## [1] 1.920183

Number of attendance

var(dfStudent$totala)

## [1] 0.7034884

IQR(dfStudent$totala)

## [1] 1

sd(dfStudent$totala)

## [1] 0.8387421

Number of homework submitted

var(dfStudent$totalhw)

## [1] 0.7965116

IQR(dfStudent$totalhw)

## [1] 1

sd(dfStudent$totalhw)

## [1] 0.892475

4.Relation Between:

Gender and USG

plot(dfStudent$gender,dfStudent$USG, xlab= "Gender", ylab="USG")

2nd and USG

plot(dfStudent$X2nd, dfStudent$USG, pch=16, xlab="2nd", ylab="USG")

Attendance and result

plot(dfStudent$totala, dfStudent$exam, pch=16, xlab="Attendance", ylab="Results")

Attendance and USG

plot(dfStudent$totala,dfStudent$USG, pch=16, xlab="Attendance", ylab="USG")

Homework submitted and USG

plot(dfStudent$totalhw,dfStudent$USG, pch=16, xlab="Homework Submitted",ylab="USG")

5.Correlation Analysis

a.the correlation between any of the variables NrSubmittedHomeworkAssignments (or any shorter name), NrAttendances and the target variable Exam (expressing the exam result)

correl <-cor(dfStudent[,c("totalhw","exam","totala")])
correl

##           totalhw      exam    totala
## totalhw 1.0000000 0.6649499 0.8155232
## exam    0.6649499 1.0000000 0.6642296
## totala  0.8155232 0.6642296 1.0000000

b. show the correlations in a corrgram and in a corrplot. You need to install the proper R packages to be able to use these.

library(corrgram)

## Warning: package 'corrgram' was built under R version 3.2.4

corrgram(correl,order = TRUE, lower.panel = panel.shade, upper.panel = panel.pie,text.panel = panel.txt, main="Student Doing Home Work, Student Attendance and Exam Results Correlation ")

library(corrplot)

## Warning: package 'corrplot' was built under R version 3.2.4

corrplot(correl,main="Student Doing Home Work, Student Attendance and Exam Results Correlation " )

Data Analysis Student Results

D.H Le

March 31, 2016

Exercise Week 5 + 6

Loading the Data

1. Frequency distribution for

Gender

Secondary School

Age Class (17-<20; 20-<23; 23-<)

Attendance (0-<2; 2-<4; 4; 5)

Homework Submitted (0-<2; 2-<4; 4; 5)

2. central tendencies for

Results

Number of attendance

Number of homework submitted

3.data dispersity for

Results

Number of attendance

Number of homework submitted

4.Relation Between:

Gender and USG

2nd and USG

Attendance and result

Attendance and USG

Homework submitted and USG

5.Correlation Analysis

a.the correlation between any of the variables NrSubmittedHomeworkAssignments (or any shorter name), NrAttendances and the target variable Exam (expressing the exam result)

b. show the correlations in a corrgram and in a corrplot. You need to install the proper R packages to be able to use these.