This post is following of above post.
In this post, let's compare men self-employed and women self-employed.
Firstly, I make two vectors, "men" and "women"
Let's see both summary statistics.
men average is 8.4 and women average is 5.7.
Let's check if the both variance are same ot not. I use var.test() function.
p-value is 1.134e-08, which is almost 0. so variance is not same.
Next, let's check if distribution location is same. I use wilcox.test() function.
p-value is less than 2.2e-16, so men and womrn location is not equal.
So far, I know men and women are different characeristics so, it is better to analyze men and women in separate.
I make a new data frame, which has both men and women variables.
Let's see summary() function results.
Now, we see men's avegrage is 9.21 and women's average is 5.73.
Let's check var.test() for if varaince is same or not.
We see small enough p-value, so men variance and women variance is not same.
How about location?
p-value is almost 0, so men and women are different location shift.
I can say, in general men young self-employed ratio is higher than women uoung self-employed ratio.
Let's see scatter plot.
men and women has positive correlation.
That's it. Thank you!
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