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











2015년 12월 19일 토요일

구에 무작위로 화살 꽂기-generating random points on a sphere







이것이 왜 필요할까요?
어떤 분이 질문하셨던 문제입니다. 

if you want to have this excel file...

https://drive.google.com/file/d/0B0ETb2rCxDW2a2hsQWdYR21zRlU/view?usp=sharing

it's free for your study.
==============================
let me think a ball(unit sphere, r=1)
if you choose random latitudes and random longitudes
it will result in an uneven distribution, 
with the density increasing as we get closer to the poles.
So I want make a map.
this will be easy for even a student to understand.

if there is a ball. which r=1
, the circle is as long as 2π
if you choose a point on surface, which has θ in latitudes
the horizontal diameter will be cosθ

and the new circle which the the point will have a length 2πcosθ

if you spread the surface of the ball flat, while maintain the area.
you will get this picture,
this is a identical to cosθ curve actually.


Now choose several points randomly
Point A is in red which means out of the Ball
Point B is in blue which means on the surface of the Ball
we will take Point B 
and ignore Point A

let me thick, Point X

if Point X =(x, y)
you will get this picture.


Can you get latitudes  and random longitudes?
it is easy enough, a school test.
x:2πcosy=x’:2π
then
x’=x/cosy





shortly

YOU can get 

































2015년 12월 7일 월요일

아빠가 들려 주는 [통계] logistic regression등 다변수 분석에서 단위 조심





독립변수들끼리 비교를 위해서는 
표준화 계수가 필요하다는 개념 설명.....

아빠가 들려 주는 [통계]logistic regression 준비작업과 산비도







웃기는 이름 산비도?
사실 산점도에 대비해서, 그냥 만들어본 이름이죠, 

어떤 분은 무슨 사이비 무술 이름 같다고 느끼실지도 모르죠?

그냥 회귀분석에서 산점도를 항상 그려 보아야 하듯이, 
비율도 그려 보아야 한다.. 로지스틱 회귀분석에서는...
그 정도로 이해하시면 좋겠습니다.

Father’s Story of [Statistics]
No P
-non inferiority trial-
Kim JeeHyoung