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# scipy stats.chi () | 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 continuous random variable

Special cases:

• chi (1, loc, scale) = full normal
• chi (2, 0, scale) = rayleigh
• Chi (3, 0, scale): Maxwell

Code # 1: Generating a continuous random Chi

 ` # scipy import ` ` from ` ` scipy.stats ` ` import ` ` chi `   ` numargs ` ` = ` ` chi. numargs ` ` [a] ` ` = ` ` [` ` 0.6 ` `,] * numargs `` rv = chi (a)   print ( "RV:" , rv) `

Exit:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x000002948537C6D8" `

Code # 2: Random Variations and Probability Distribution.

 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 ` `, ` ` 0.1 ` `) `   ` # Random Variants ` ` R ` ` = ` ` chi.rvs (a, scale ` ` = ` ` 2 ` `, size ` ` = ` ` 10 ` `) ` ` print ` ` (` ` "Random Variates:" ` `, R) `   ` # PDF ` ` R ` ` = ` ` chi.pdf (a, quantile, loc ` ` = ` ` 0 ` `, scale ` ` = ` ` 1 ` `) ` ` print ` ` (` ` "Probability Distribution:" ` `, R) `

Output:

` Random Variates: [2.40483665 1.68478304 0.01664071 2.48977805 3.66286843 1.68463842 0.14434643 0.67812242 0.46190886 1.99973997] Probability193 0.14 0.25719966 0.35519439 0.43801475 0.50641521 0.56131243 0.60373433 0.63477687 0.6555 6791] `

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 ` ` = ` ` plt.plot (distribution, rv.pdf (distribution)) `

Output:

` Distribution: 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 ` ` = ` ` chi.pdf (x, ` ` 1 ` `, ` ` 6 ` `) ` ` y2 ` ` = ` ` chi.pdf (x , ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` `" * "` < code class = "plain">, x, y2, ` "r--" ` `) `

Output:

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