uweziegenhagen.de

# -*- coding: utf-8 -*-
"""
Created on Sat Mar 25 06:52:11 2017
@author: Uwe Ziegenhagen
"""
 
sum = 0
factor = -1
 
for i in range(1, 640000, 2):
        sum = sum + factor * 1/i
        factor *= -1
        # print(sum)
 
print(-4*sum)

# -*- coding: utf-8 -*-
"""
Created on Mon Mar 13 21:14:17 2017
@author: Uwe Ziegenhagen, ziegenhagen@gmail.com
 
Creates a CDF table for the standard normal distribution
 
use booktabs package in the preamble and put 
the generated numbers inside (use only one backslash!)
 
\\begin{tabular}{r|cccccccccc} \\toprule
<output here>
\\end{tabular}
"""
 
from scipy.stats import norm
 
print(norm.pdf(0))
print(norm.cdf(0),'\r\n')
 
horizontal = range(0,10,1)
vertikal = range(0,37)
 
header = ''
for i in horizontal:
    header = header + '& ' + str(i/100)
 
print(header, '\\\\ \\midrule')
 
for j in vertikal:  
    x = j/10
    print('\\\\', x)
    for i in horizontal:
        y = x + i/100
        print('& ', "{:10.4f}".format(norm.cdf(y),4))
 
 
print('\\\\ \\bottomrule \r\n')

confInt <- function(runs){
	result<-NULL
	for (i in 1:runs) {
		data<-rnorm(10000)
		n<-length(data)
		a<-mean(data)
		s<-sd(data)
		error <- qnorm(0.975)*s/sqrt(n)
		left <- a-error
		right <- a+error
		result[i] = left<0 & 0<right
	}
	result
}
summary(confInt(100))

confInt <- function(runs){
	x<-1:runs
	mu<-NULL
	sigma<-NULL
	result<-NULL
	vleft<-NULL
	vright<-NULL
 
	for (i in 1:runs) {
		data<-rnorm(1000)
		n<-length(data)
		a<-mean(data)
		mu[i]<-a
		s<-sd(data)
		sigma[i]<-s
		error <- qnorm(0.975)*s/sqrt(n)
		left <- a-error
		right <- a+error
 
		result[i] = left<0 & 0<right
		vleft[i] = left	
		vright[i] = right
}
	data.frame(x,mu,sigma,result,vleft,vright)
}
 
 
df<-confInt(100)
 
require(ggplot2)
 
myplot<-ggplot(df, aes(x = x, y = mu)) +
  geom_point(size = 2) +
  geom_errorbar(aes(ymax = vleft, ymin = vright,colour=result*3))
myplot + theme_bw() 
summary(df)

DROP TABLE #data
CREATE TABLE #data(NUMBER FLOAT)
 
INSERT INTO #data VALUES(1.0);INSERT INTO #data VALUES(2.0);
INSERT INTO #data VALUES(3.0);INSERT INTO #data VALUES(4.0);
INSERT INTO #data VALUES(5.0);INSERT INTO #data VALUES(6.0);
INSERT INTO #data VALUES(7.0);INSERT INTO #data VALUES(8.0);
INSERT INTO #data VALUES(9.0);INSERT INTO #data VALUES(10.0);
INSERT INTO #data VALUES(11.0);INSERT INTO #data VALUES(12.0);
INSERT INTO #data VALUES(13.0);INSERT INTO #data VALUES(14.0);
INSERT INTO #data VALUES(15.0);INSERT INTO #data VALUES(16.0);
INSERT INTO #data VALUES(17.0);INSERT INTO #data VALUES(18.0);
INSERT INTO #data VALUES(19.0);INSERT INTO #data VALUES(20.0);
INSERT INTO #data VALUES(21.0);INSERT INTO #data VALUES(22.0);
INSERT INTO #data VALUES(23.0);INSERT INTO #data VALUES(24.0);
INSERT INTO #data VALUES(25.0);INSERT INTO #data VALUES(26.0);
INSERT INTO #data VALUES(27.0);INSERT INTO #data VALUES(28.0);
INSERT INTO #data VALUES(29.0);INSERT INTO #data VALUES(30.0);
INSERT INTO #data VALUES(31.0);INSERT INTO #data VALUES(32.0);
INSERT INTO #data VALUES(33.0);INSERT INTO #data VALUES(34.0);
INSERT INTO #data VALUES(35.0);INSERT INTO #data VALUES(36.0);
INSERT INTO #data VALUES(37.0);INSERT INTO #data VALUES(38.0);
INSERT INTO #data VALUES(39.0);INSERT INTO #data VALUES(40.0);
INSERT INTO #data VALUES(-100.0);INSERT INTO #data VALUES(100.0);
 
DECLARE @median FLOAT
DECLARE @perc25 FLOAT
DECLARE @perc75 FLOAT
DECLARE @iqr FLOAT
 
SET @median = (SELECT   MAX(NUMBER) AS Median FROM (SELECT TOP 50 PERCENT NUMBER FROM #data ORDER BY NUMBER) a)
SET @perc25 = (SELECT   MAX(NUMBER) AS Median FROM (SELECT TOP 25 PERCENT NUMBER FROM #data ORDER BY    NUMBER) a)
SET @perc75 = (SELECT   MAX(NUMBER) AS Median FROM (SELECT TOP 75 PERCENT NUMBER FROM #data ORDER BY    NUMBER) a)
 
print @median
print @perc25
print @perc75
 
SET @iqr = @perc75-@perc25
print 'IQR'
print @iqr
 
SELECT * FROM #data WHERE NUMBER <  (@median + 1.5* @iqr) AND 
NUMBER >  (@median - 1.5* @iqr)
 
print 'number of outliers'
SELECT COUNT(*) FROM #data WHERE NUMBER <  (@median - 1.5* @iqr)
OR NUMBER >  (@median + 1.5* @iqr)