레이블이 chi-square test인 게시물을 표시합니다. 모든 게시물 표시
레이블이 chi-square test인 게시물을 표시합니다. 모든 게시물 표시

2016년 6월 28일 화요일

아빠가 들려 주는 [통계] 카이제곱 검정 & Fisher exact test

얼마전에도 같은 글을 올렸네요.. 그러고 보니..
오늘은 동영상으로 글 올립니다. 
오늘 네이버 블로그에 질문 올리신 분이 있어서 답이 되길 바라면서..

2015년 12월 27일 일요일

[real statistics] All of chi-squre test


at first down load this file here for nothing,




zoom out the sheet.
(1) what is chi- square test and Pearson and Yates
(2) chi-square distribution
(3) Odds ratio, Risk Ratio, Risk Difference and their 95% confidence interval
(4) Phi and Cramer's V
(5) some charts fit to chi- square test. you can copy and paste Word or PowerPoint and modify them easily.


(6) only fill new number Yellow Cells!!!! Do not change other cells



And Now we follow the old man's thought


we make the final number.
the number is "chi-square"
Who made this number? Pearson made it.
The son of Pear? Not actually he is the father of Statistics.

The larger this number, the bigger the difference between expected and observed.
This is Pearson's thought and it is reasonable.


Now he made a nice conclusion.
the possibility that two table is same = p
p=0.005~ so two table is not same.

One scholar named Yates made a small change the number X2
So we call this new number 'Yates X2'
'Yates X2' is more accurate when the cell is small.
if the cell is large, Two X2 get closer.

  
yes we say the possibility be p=0.005

but "How much different"
there are many ways
(1) odds ratio
(2) risk ratio(=relative risk)
(3) risk difference
(4) Cramer V and phi

you can choose one in your paper and power point.
(1) odds ratio
     usually for cross-sectional study
     odds itself ratio between two observation.
(2) risk ratio(=relative risk)
     usually for cohort study
     risk usually include observation after time(period)
(3) risk difference
     usually for cohort study
     risk usually include observation after time(period)
     relatively no so popular but increasing
     especially for non-inferiority test

all three are written with it 95% confidence interval


(4) Cramer V and phi
     two values are same (when 2X2 table)
     not so common
     similar to correlation coefficient



Three chart are easy to understand.
you can copy and paste in your paper(MS word) and slide(powerpoint)
and modify them