Change language

# scipy stats.f () | python

|

scipy.stats.f () — it is a continuous random variable F that is defined with a standard format and some shape parameters to complete its specification.

Parameters:
q: lower and upper tail probability
a, b: shape parameters
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: F continuous random variable

Code # 1: Create F continuous random variable values ​​

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

Output:

` RV: "scipy.stats. _distn_infrastructure.rv_frozen object at 0x0000018D566864A8" `

Code # 2: exponential F of random variables and probability distribution.

` `

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

` `

Output:

` Random Variates: [2.77609532e + 00 2.55454726e- 04 7.77303742e + 01 2.61642158e + 02 3.39772973e-01 8.63437666e + 02 3.24316832e + 02 5.88915362e + 06 1.27105242e + 03 7.30691909e-01] Probability Distribution: [0.00800042 0.06746857 0.10587056 0.13291306 0 .15295841 0.16837285 0.18056559 0.19043041 0.19856155 0.2053691] `

Code # 3: Graphic representation.

 ` import ` ` numpy as np ` ` import ` ` matplotlib.pyplot as plt `   ` distribution ` ` = ` ` np.linspace (` ` 0 ` `, np.minimum (rv .dist.b, ` ` 3 ` `)) ` ` print ` ` (` ` "Distribution:" ` `, 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: Various Positional Arguments

 ` import ` ` matplotlib. pyplot as plt ` ` import ` numpy as np   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 5 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` f .pdf (x, ` ` 2 ` `, ` ` 6 ` `) ` ` y2 ` ` = ` ` f .pdf (x , ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` `" * "` , x, y2, ` "r--" ` `) `

Output:

## Shop

Learn programming in R: courses

\$

Best Python online courses for 2022

\$

Best laptop for Fortnite

\$

Best laptop for Excel

\$

Best laptop for Solidworks

\$

Best laptop for Roblox

\$

Best computer for crypto mining

\$

Best laptop for Sims 4

\$

Latest questions

NUMPYNUMPY

Common xlabel/ylabel for matplotlib subplots

NUMPYNUMPY

How to specify multiple return types using type-hints

NUMPYNUMPY

Why do I get "Pickle - EOFError: Ran out of input" reading an empty file?

NUMPYNUMPY

Flake8: Ignore specific warning for entire file

NUMPYNUMPY

glob exclude pattern

NUMPYNUMPY

How to avoid HTTP error 429 (Too Many Requests) python

NUMPYNUMPY

Python CSV error: line contains NULL byte

NUMPYNUMPY

csv.Error: iterator should return strings, not bytes

## Wiki

Python | How to copy data from one Excel sheet to another

Common xlabel/ylabel for matplotlib subplots

Check if one list is a subset of another in Python

How to specify multiple return types using type-hints

Printing words vertically in Python

Python Extract words from a given string

Cyclic redundancy check in Python

Finding mean, median, mode in Python without libraries