****************************************************************************
    Three categories:
      * 1st category (<=5 years): 1st (lower) quartile;
      * 2nd category (>5 and > 12.25 years): inter quartile range;
      * 3rd category (>=12.25 years): 3rd (upper) quartile;
    ****************************************************************************

# Creating matrix: years of programming experience vs. ratings

ratings1_1_YearsProg <- matrix(c(10,21,14, 1,2,1, 10,15,5, 0,1,0), nrow = 4, ncol = 3, byrow = TRUE, dimnames = list(c("1st more cohesive", "2nd more cohesive", "Equally coh", "Don't know"), c("<=5", ">5 AND <12.25", ">=12.25")))

> ratings1_1_YearsProg
                    <=5 >5 AND <12.25 >=12.25
1st more cohesive    10            21      14
2nd more \ncohesive   1             2       1
Equally coh          10            15       5
Don't know            0             1       0
> 


ratings2_1_YearsProg <- matrix(c(14,24,16, 3,5,2, 4,9,2, 0,1,0), nrow = 4, ncol = 3, byrow = TRUE, dimnames = list(c("1st more cohesive", "2nd more cohesive", "Equally coh", "Don't know"), c("<=5", ">5 AND <12.25", ">=12.25")))

> ratings2_1_YearsProg
                  <=5 >5 AND <12.25 >=12.25
1st more cohesive  14            24      16
2nd more cohesive   3             5       2
Equally coh         4             9       2
Don't know          0             1       0
> 


ratings3_1_YearsProg <- matrix(c(2,4,0, 10,19,14, 9,15,6, 0,1,0), nrow = 4, ncol = 3, byrow = TRUE, dimnames = list(c("1st more cohesive", "2nd more cohesive", "Equally coh", "Don't know"), c("<=5", ">5 AND <12.25", ">=12.25")))

> ratings3_1_YearsProg
                    <=5 >5 AND <12.25 >=12.25
1st more cohesive     2             4       0
2nd more \ncohesive  10            19      14
Equally coh           9            15       6
Don't know            0             1       0



# Executing the Fisher exact test between Years of Prog. Exp. and Ratings

> fisher.test(ratings1_1_YearsProg, simulate.p.value = TRUE)

        Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates)

data:  ratings1_1_YearsProg
p-value = 0.7716
alternative hypothesis: two.sided

> fisher.test(ratings1_1_YearsProg)

        Fisher's Exact Test for Count Data

data:  ratings1_1_YearsProg
p-value = 0.7688
alternative hypothesis: two.sided

> 


> fisher.test(ratings2_1_YearsProg, simulate.p.value = TRUE)

        Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates)

data:  ratings2_1_YearsProg
p-value = 0.8851
alternative hypothesis: two.sided

> 

> fisher.test(ratings2_1_YearsProg)

        Fisher's Exact Test for Count Data

data:  ratings2_1_YearsProg
p-value = 0.8749
alternative hypothesis: two.sided

> 



> fisher.test(ratings3_1_YearsProg, simulate.p.value = TRUE)

        Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates)

data:  ratings3_1_YearsProg
p-value = 0.5867
alternative hypothesis: two.sided


> fisher.test(ratings3_1_YearsProg)

        Fisher's Exact Test for Count Data

data:  ratings3_1_YearsProg
p-value = 0.5863
alternative hypothesis: two.sided

>