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# 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: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029485196DD8" `

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:

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