Month: April 2017

Mean +/- 1.96σ/√n -> normal
μ=1/N Σ Xi

P(μ

Confidence　Intervals

60% partyA +-3% -> confidence interval 57, 63 in %.
many often confidence interval become 95% chance.

suppose we increase the sample size N, size of CI shrink.

P=0.5, μ=0.5, σ^2=0.25
mean(ΣXi), Var(ΣXi), Var(1/nΣXi),　std dev, CI
n=1, 0.5, 0.25, 0.25, 0.5, 0.98
n=2, 1, 0.5, 0.125, 0.35, 0.69
n=10, 5, 2.5, 0.025, 0.16, 0.31
1.96 magic number

π 3.14
e 2.718

# remove outliers
# extract data between lower and upper quartile

# fit Gaussian using MLE

# compute x that corresponds to standard score z
return x

import random
from math import sqrt

def mean(data):
	return sum(data)/len(data)

def variance(data):
	mu=mean(data)
	return sum([(x-mu)**2 for x in data])/len(data)

def stddev(data):
	return sqrt(variance(data))

weight=[80.,85,200,85,69,65,68,66,85,72,85,82,65,105,75,80,
    70,74,72,70,80,60,80,75,80,78,63,88.65,90,89,91,1.00E+22,
    75,75,90,80,75,-1.00E+22,-1.00E+22,-1.00E+22,86.54,67,70,92,70,76,81,93,
    70,85,75,76,79,89,80,73.6,80,80,120,80,70,110,65,80,
    250,80,85,81,80,85,80,90,85,85,82,83,80,160,75,75,
    80,85,90,80,89,70,90,100,70,80,77,95,120,250,60]

print mean(weight)

def calculate_weight(data, z):
	data.sort()
	lowerq = (len(data)-3)/4
	upperq = lowerq * 3 + 3
	newdata = [data[i] for i in range(lowerq, upperq)]

	mu = mean(newdata)
	sigma = stddev(newdata)

	x = mu + z * sigma
	return x

print calculate_weight(weight, -2.)

Binomial Distribution
->
Central Limit Theorem
->
Normal Distribution

f(x)=l^[-1/2(x-μ)^2/σ^2]

Manipulating Normals
(aX+b)
μ,σ^2, aμ+b,a^2σ^2
(X+Y)
μ,σ^2 μσ^2, μ+μ, σ^2σ^2

coin:(0,1) P(Σi=k)= n!/(n-k)!k!
Pascal Triangle

flip a coin 1000 times
mean
standard deviation

import random
from math import sqrt

def mean(data):
	return float(sum(data))/len(data)

def variance(data):
	mu=mean(data)
	return sum([(float(x)-mu)**2 for x in data])/len(data)

def stddev(data):
	return sqrt(variance(data))

def flip(N):
    return [random.random() > 0.5 for x in range(N)]

N=1000
f=flip(N)

print mean(f)
print stddev(f)

n! / (n-k)!・k!

p(heads)=0.5
flip coin 5 times
p(#head)=1
5!/4!*1! = 5, 2^5 = 32
0.15625

probability p(heads)=0.8
frip coin 3 time p(#heads= 1)
0.096

5*(0.8)^4 *(0.2)^1

outlier: 外れ値

real age -> age in database
type ->

diminish type from target

lower quarlile
upper quarlile
interquarlile range

percentile
20,21,22,24,211
upper 20% percentile

from math import sqrt

data3=[13.04, 1.32, 22.65, 17.44, 29.54, 23.22, 17.65, 10.12, 26.73, 16.43]


def mean(data):
    return sum(data)/len(data)
def variance(data):
    mu=mean(data)
    return mean([(x-mu)**2 for x in data])
def stddev(data):
	sigma2 = variance(data)
	return sqrt(sigma2)

print stddev(data3)

standard score(偏差値) = (data – mean)/standard deviation

xi…,xi…,xn
mean:5
variance:16
standard deviation:4
xi:9

multiply by 1.5

standard score:(9-5)/4 = 1
μ:7.5
σ:6
σ^2:36
yi:13.5
z:1

correction factor(補正率)

incremental mean

from __future__ import division

def mean(oldmean, n, x):
	return (oldmean*n+x)/(n+1)

currentmean=10
currentcount=5
new=4

print mean(currentmean, currentcount,new)

def likelihood(dist,data):
l = 1
for i in data:
l*dist[i]
return l

tests= [(({‘A’:0.2,’B’:0.2,’C’:0.2,’D’:0.2,’E’:0.2},’ABCEDDECAB’), 1.024e-07),(({‘Good’:0.6,’Bad’:0.2,’Indifferent’:0.2},[‘Good’,’Bad’,’Indifferent’,’Good’,’Good’,’Bad’]), 0.001728),(({‘Z’:0.6,’X’:0.333,’Y’:0.067},’ZXYYZXYXYZY’), 1.07686302456e-08),(({‘Z’:0.6,’X’:0.233,’Y’:0.067,’W’:0.1},’WXYZYZZZZW’), 8.133206112e-07)]

for t,l in tests:
if abs(likelihood(*t)/l-1)<0.01: print 'Correct' else: print 'Incorrect' [/python]

data2 = []
def variance

return

print variance(data2)

mean =
data = []
ndata = data – mu
ndata = []
ndata.append()

data3=[13.04, 1.32, 22.65, 17.44, 29.54, 23.22, 17.65, 10.12, 26.73, 16.43]
def mean(data):
    return sum(data)/len(data)
def variance(data):
	mu = mean(data)
	ndata = []
	for i in range(len(data)):
		ndata.append((data[i] - mu)**2)
	sigma2 = mean(ndata)
	return sigma2	

another simple pattern comes here

data3=[13.04, 1.32, 22.65, 17.44, 29.54, 23.22, 17.65, 10.12, 26.73, 16.43]
def mean(data):
    return sum(data)/len(data)
def variance(data):
	mu = mean(data)
	return mean([(x-mu)**2 for x in data])

print variance(data2)

mean

data1=[49., 66, 24, 98, 37, 64, 98, 27, 56, 93, 68, 78, 22, 25, 11]

def mean(data):
	return sum(data)/len(data)

print mean(data1)

median

data1=[1,2,5,10,-20]
def median(data):
	sdata = sorted(data)
	index = (len(data) - 1)/2
	return sdata[index]

print median(data1)

data1 = [1,2,5,10,-20,5,5]

def mode(data):
…
return

print mode(data1)
5

for i in range(len(data))
if :
data.count(data.[i])

data1=[1,2,5,10,-20,5,5]
def mode(data):
	modecnt=0
	for i in range(len(data))
		icount=data.count(data.[i])
		if icount>modecnt:
			mode=data[i]
			modecnt=icount
	return mode

print mode(data1)