表白失败的打击有多大？# Piebridge - 鹊桥
t*n
1 楼
没有人发过这个吧？
我就copy了前半部分。 后边还有很多统计图表，打开链接自己看，挺好玩的。
版主给我发个双黄包吧。
http://rpubs.com/columbia202/27405
Using the information that users have shared publicly on trackitt.com, I
made some graphs and did some basic hypothesis testing. In order to get the
data, I simply scraped all the webpages using R, and cleaned the data by
removing incomplete cases.
Percentage of applicants in each category, and the correspondent percentages
of accepted, pending, and denied cases
## types percentage accepted pending denied
## 5 EB1A 3.358 71.84 17.352 10.350
## 6 EB1B 3.900 92.14 5.505 2.359
## 1 EB1C 7.498 82.41 15.951 1.431
## 2 EB2 58.137 91.59 7.104 1.090
## 3 EB2-NIW 6.328 83.84 13.651 1.939
## 4 EB3 20.778 89.37 7.577 2.706
Countries leading in the number of applicants
## EB2-NIW ##
## India China South Korea Iran Taiwan Pakistan
## 22.536 16.721 9.855 7.431 4.281 2.342 #
# All categories ##
## India China South Korea Pakistan Philippines Canada
## 63.356 6.226 2.730 2.152 1.983 1.769
How about checking if there is any significant difference between countries,
in NIW category?
## types percentage accepted pending denied
## 1 South Korea 15.601 87.70 12.295 0.000
## 2 India 35.678 82.08 15.054 2.509
## 3 China 26.471 89.37 8.696 1.932
## 4 Iran 11.765 79.35 18.478 2.174
## 5 Taiwan 6.777 86.79 11.321 1.887
## 6 Pakistan 3.708 79.31 13.793 6.897
Seems like there is a difference between Pakistan and South Korea (pvalue =
0.0383)! But if we correct for multiple-hypothesis testing, the difference
is not significant.
What about for all categories?
## types percentage accepted pending denied
## 1 India 81.002 89.05 8.770 1.9524
## 2 South Korea 3.490 90.64 8.989 0.1873
## 3 Pakistan 2.751 85.99 8.789 3.8005
## 4 Philippines 2.536 82.99 14.948 2.0619
## 5 China 7.960 90.48 8.128 1.3957
## 6 Canada 2.261 91.33 7.225 1.1561
And there is no difference between any other countries.
Focusing on the approved case, I was curious to see what’s the difference
between the total time it takes to be approved between different categories.
Categories are further divided by their processing speed.
我就copy了前半部分。 后边还有很多统计图表，打开链接自己看，挺好玩的。
版主给我发个双黄包吧。
http://rpubs.com/columbia202/27405
Using the information that users have shared publicly on trackitt.com, I
made some graphs and did some basic hypothesis testing. In order to get the
data, I simply scraped all the webpages using R, and cleaned the data by
removing incomplete cases.
Percentage of applicants in each category, and the correspondent percentages
of accepted, pending, and denied cases
## types percentage accepted pending denied
## 5 EB1A 3.358 71.84 17.352 10.350
## 6 EB1B 3.900 92.14 5.505 2.359
## 1 EB1C 7.498 82.41 15.951 1.431
## 2 EB2 58.137 91.59 7.104 1.090
## 3 EB2-NIW 6.328 83.84 13.651 1.939
## 4 EB3 20.778 89.37 7.577 2.706
Countries leading in the number of applicants
## EB2-NIW ##
## India China South Korea Iran Taiwan Pakistan
## 22.536 16.721 9.855 7.431 4.281 2.342 #
# All categories ##
## India China South Korea Pakistan Philippines Canada
## 63.356 6.226 2.730 2.152 1.983 1.769
How about checking if there is any significant difference between countries,
in NIW category?
## types percentage accepted pending denied
## 1 South Korea 15.601 87.70 12.295 0.000
## 2 India 35.678 82.08 15.054 2.509
## 3 China 26.471 89.37 8.696 1.932
## 4 Iran 11.765 79.35 18.478 2.174
## 5 Taiwan 6.777 86.79 11.321 1.887
## 6 Pakistan 3.708 79.31 13.793 6.897
Seems like there is a difference between Pakistan and South Korea (pvalue =
0.0383)! But if we correct for multiple-hypothesis testing, the difference
is not significant.
What about for all categories?
## types percentage accepted pending denied
## 1 India 81.002 89.05 8.770 1.9524
## 2 South Korea 3.490 90.64 8.989 0.1873
## 3 Pakistan 2.751 85.99 8.789 3.8005
## 4 Philippines 2.536 82.99 14.948 2.0619
## 5 China 7.960 90.48 8.128 1.3957
## 6 Canada 2.261 91.33 7.225 1.1561
And there is no difference between any other countries.
Focusing on the approved case, I was curious to see what’s the difference
between the total time it takes to be approved between different categories.
Categories are further divided by their processing speed.
