scipy.stats.chi2 () | python

Parameters:
q: lower and upper tail probability
x: quantiles
loc : [optional] location parameter. Default = 0
scale: [optional] scale parameter. Default = 1
size: [tuple of ints, optional] shape or random variates.
moments: [optional] composed of letters [`mvsk `]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: chi squared continuous random variable

Code # 1: Generate continuous random hee values ​​

# scipy import

from scipy.stats import chi2

 

numargs = chi2.numargs

[a] = [ 0.6 ,] * numargs

rv = chi2 (a)

  

print ( " RV: " , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029485196DD8 & gt; 

Code # 2: chi2 random variables and probability distribution function.

import numpy as np

quantile = np.arange ( 0.01 , 1 , 0.1 )

 
# Random Variants

R = chi2.rvs (a, scale = 2 , size = 10 )

prin t ( "Random Variates:" , R)

 
# PDF

R = chi2.pdf (a, quantile, loc = 0 , scale = 1 )

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [6.20115012e-01 4.82717678e-01 1.43760444e -02 1.19755537e + 00 3.00093606e-05 6.11268950e-01 5.99940774e-01 3.20509994e-01 1.94220599e-01 6.63225404e-01] Probability Distribution: [0.00615404 0.06544849 0.12034254 0.1704933 0.21568 622 0.25581903 0.29088625 0.32096438 0.34619796 0.36678666] 

Code # 3: Graphic representation.

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum (rv. dist.b, 5 ))

print ( "Distribution:" , distribution)

 

plot = pl t.plot (distribution, rv.pdf (distribution))

Output:

 Distribution: [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ] 

Code # 4: Various Positional Arguments

import matplotlib. pyplot as plt

import numpy as np

 

x = np.linspace ( 0 , 5 , 100 )

 
# Various positional arguments

y1 = chi2.pdf (x, 1 , 6 )

y2 = chi2.pdf (x, 1 , 4 )

plt.plot (x, y1, "*"  , x, y2, "r--" )

Output: