scipy stats.gilbrat () | python

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

Results: Gilbrat continuous random variable

Code # 1: Generating a continuous random variable Gilbrat

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A3B4AC8 & gt; 

Code # 2: Gilbrat random variables and probability distribution

Output:

 Random Variates: [0.66090031 1.39027118 1.33876164 1.50366592 5.21419497 5.24225463 3.98547687 0.30586938 9.11346685 0.93014057] Probability Distribution: [0.00099024 0.31736749 0.5620854 0.64817773 0.65389139 0.62357239 0.57879516 0.52988354 0.48170703 0.43645277] 

Code # 3: Graphic representation

Output:

 Distribution: [0. 0.06122449 0.12244898 0.18367347 0.24489796 0.30612245 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449 0.67346939 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633 1.10204082 1.16326531 1.2244898 1.28571429 1.34693878 1.40816327 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714 2.20408163 2.26530612 2.32653061 2.3877551 2.44897959 2.51020408 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102 2.93877551 3. ] 

Code # 4: Various Positional Arguments

Output:

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