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# Python – Logistic Distribution in Statistics

scipy.stats.logistic() is a logistic (or Sech-squared) continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution.

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 : Logistic (or Sech-squared) continuous random variable

Code #1 : Creating logistic (or Sech-squared) continuous random variable

 `# importing library`` ` `from` `scipy.stats ``import` `logistic  ``   ` `numargs ``=` `logistic.numargs ``a, b ``=` `4.32``, ``3.18``rv ``=` `logistic(a, b) ``   ` `print` `(``"RV : \n"``, rv)  `

Output :

```RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D691AF48
```

Code #2 : logistic (or Sech-squared) continuous variates and probability distribution

 `import` `numpy as np ``quantile ``=` `np.arange (``0.03``, ``2``, ``0.21``) `` ` `# Random Variates ``R ``=` `logistic.rvs(a, b) ``print` `(``"Random Variates : \n"``, R) `` ` `# PDF ``R ``=` `logistic.pdf(a, b, quantile) ``print` `(``"\nProbability Distribution : \n"``, R) `

Output :

```Random Variates :
2.655712569915262

Probability Distribution :
[3.09335001e-48 2.86886306e-04 2.07194337e-02 7.76003172e-02
1.34094156e-01 1.71154982e-01 1.89845362e-01 1.96114490e-01
1.95030508e-01 1.89935182e-01]

```

Code #3 : Graphical Representation.

 `import` `numpy as np ``import` `matplotlib.pyplot as plt ``    ` `distribution ``=` `np.linspace(``0``, np.minimum(rv.dist.b, ``3``)) ``print``(``"Distribution : \n"``, distribution) ``    ` `plot ``=` `plt.plot(distribution, rv.pdf(distribution)) `

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 : Varying Positional Arguments

 `import` `matplotlib.pyplot as plt ``import` `numpy as np ``    ` `x ``=` `np.linspace(``0``, ``5``, ``100``) ``    ` `# Varying positional arguments ``y1 ``=` `logistic .pdf(x, ``1``, ``3``) ``y2 ``=` `logistic .pdf(x, ``1``, ``4``) ``plt.plot(x, y1, ``"*"``, x, y2, ``"r--"``) `

Output : My Personal Notes arrow_drop_up