4HuNNid

 #1 enter the data in the software
student<-c("ZIM", "DRC", "BOTS", "ANG", "SA", "DRC", "MOZ", "ZIM", "SA", "MAL", "ZIM", "ZIM", "MOZ", "ANG", "BOTS", "ZIM", "MOZ", "MAL", "SA", "ZIM", "DRC", "MOZ", "DRC", "MAL", "MAL", "NAM", "MOZ", "SA", "MAL", "BOTS", "LES", "MAL", "DRC", "MAL", "BOTS", "SA", "SA", "LES", "MAL", "LES", "LES", "SA", "ZIM", "ZIM", "SA", "MOZ", "DRC", "ANG" )
#2 PRESENT THE DATA ON A FREQUENCY DISTRIBUTION TABLE
freq_table<-table(student)
freq_table

## student
##  ANG BOTS  DRC  LES  MAL  MOZ  NAM   SA  ZIM 
##    3    4    6    4    8    6    1    8    8

#3Calculate the relative frequencies and percentages for each catagory
rel_freq<-prop.table(freq_table)
rel_freq

## student
##        ANG       BOTS        DRC        LES        MAL        MOZ        NAM 
## 0.06250000 0.08333333 0.12500000 0.08333333 0.16666667 0.12500000 0.02083333 
##         SA        ZIM 
## 0.16666667 0.16666667

percentages<-rel_freq*100
percentages

## student
##       ANG      BOTS       DRC       LES       MAL       MOZ       NAM        SA 
##  6.250000  8.333333 12.500000  8.333333 16.666667 12.500000  2.083333 16.666667 
##       ZIM 
## 16.666667

#combine into a data frame
distribution<-data.frame(
country=names(freq_table), 
frequency=as.vector(freq_table),
relative_freq=round(as.vector(rel_freq),4),
percentages=round(as.vector(percentages),2)
)
distribution

##   country frequency relative_freq percentages
## 1     ANG         3        0.0625        6.25
## 2    BOTS         4        0.0833        8.33
## 3     DRC         6        0.1250       12.50
## 4     LES         4        0.0833        8.33
## 5     MAL         8        0.1667       16.67
## 6     MOZ         6        0.1250       12.50
## 7     NAM         1        0.0208        2.08
## 8      SA         8        0.1667       16.67
## 9     ZIM         8        0.1667       16.67

pie(freq_table, main="STUDENT DISTRIBUTION BY COUNTRY", 
    col=rainbow(length(freq_table)))

  #bar chat
barplot(freq_table, main="STUDENT DISTRIBUTION BY COUNTRY",
        col="blue",
        ylab = "NUMBER OF STUDENTS")

expected<-rep(length(student)/length(freq_table),
              length(freq_table))
chisq.test(freq_table, 
p=rep(1/length(freq_table),
      length(freq_table)))

## 
##  Chi-squared test for given probabilities
## 
## data:  freq_table
## X-squared = 9.375, df = 8, p-value = 0.3117

#5 use chi squared test to determine the observed distribution of students by your country significantly differs from a hypothesized or expected uniform distribution