> d <- read.csv("personality.csv")
> d[1:3,]
  id Completed age gender pers1 ocd1 pers2 ocd2 pers3 pers4 ocd3 ocd4 pers5
1  1         Y  20      M     2    3     3    1     4     2    3    2     4
2  2         Y  28      F     2    2     2    3     3     3    3    1     2
3  3         Y  24      F     1    2     2    2     3     2    4    1     1
  ocd5
1    4
2    3
3    3
> d$pers <- d$pers1 + d$pers2 + d$pers3 + d$pers4 + d$pers5
> d[1:3,]
  id Completed age gender pers1 ocd1 pers2 ocd2 pers3 pers4 ocd3 ocd4 pers5
1  1         Y  20      M     2    3     3    1     4     2    3    2     4
2  2         Y  28      F     2    2     2    3     3     3    3    1     2
3  3         Y  24      F     1    2     2    2     3     2    4    1     1
  ocd5 pers
1    4   15
2    3   12
3    3    9
> d$ocd <- d$ocd1 + d$ocd2 + d$ocd3 + d$ocd4 + d$ocd5
> d[1:3,]
  id Completed age gender pers1 ocd1 pers2 ocd2 pers3 pers4 ocd3 ocd4 pers5
1  1         Y  20      M     2    3     3    1     4     2    3    2     4
2  2         Y  28      F     2    2     2    3     3     3    3    1     2
3  3         Y  24      F     1    2     2    2     3     2    4    1     1
  ocd5 pers ocd
1    4   15  13
2    3   12  12
3    3    9  12
> plot(d$pers, d$ocd)
> cor.test(d$pers, d$ocd)

        Pearson's product-moment correlation

data:  d$pers and d$ocd
t = 0.5949, df = 71, p-value = 0.5538
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1622677  0.2957047
sample estimates:
       cor
0.07042888

> plot(d$age, d$pers)
> cor.test(d$age, d$pers)

        Pearson's product-moment correlation

data:  d$age and d$pers
t = 1.4475, df = 71, p-value = 0.1521
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.0632198  0.3844038
sample estimates:
      cor
0.1693101

> plot(d$age,d$ocd)
> cor.test(d$age, d$ocd)

        Pearson's product-moment correlation

data:  d$age and d$ocd
t = -2.3315, df = 71, p-value = 0.02257
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.46802640 -0.03899937
sample estimates:
       cor
-0.2666739

> help(abline)
> abline(lm(d$ocd~d$age))
> cor.test(d$pers, d$ocd)

        Pearson's product-moment correlation

data:  d$pers and d$ocd
t = 0.5949, df = 71, p-value = 0.5538
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1622677  0.2957047
sample estimates:
       cor
0.07042888

> plot(d$pers, d$ocd)
> abline(lm(d$ocd~d$pers))
> d[1:3, ]
  id Completed age gender pers1 ocd1 pers2 ocd2 pers3 pers4 ocd3 ocd4 pers5
1  1         Y  20      M     2    3     3    1     4     2    3    2     4
2  2         Y  28      F     2    2     2    3     3     3    3    1     2
3  3         Y  24      F     1    2     2    2     3     2    4    1     1
  ocd5 pers ocd
1    4   15  13
2    3   12  12
3    3    9  12
> mocd <- d$ocd[d$gender=="M"]
> focd <- d$ocd[d$gender == "F"]
> wilcox.test(mocd, focd, paired=F, exact=F)

        Wilcoxon rank sum test with continuity correction

data:  mocd and focd
W = 521, p-value = 0.5209
alternative hypothesis: true location shift is not equal to 0

> mpers <- d$pers[d$gender=="M"]
> fpers <- d$pers[d$gender=="F"]
> wilcox.test(mpers, fpers, paired=F, exact=F)

        Wilcoxon rank sum test with continuity correction

data:  mpers and fpers
W = 622.5, p-value = 0.5687
alternative hypothesis: true location shift is not equal to 0

> length(mpers); length(fpers)
[1] 27
[1] 55
>