F distribution¶

Let’s investigate how shape of the F distribution curve look likes.

# import required libraries
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
from IPython.display import display

Let’s now investigate how shape of the F distribution curve changes with respect to degrees of freedom (\(\nu_1\) and \(\nu_2\)). We will define a function first to simplify our calculations.

# define a Python function with three parameters

def f_dist_curve(dof1,dof2,ltype, col):
    
    fig, ax = plt.subplots(figsize = (6, 6))
    
    #use zip for parallel iteration
    for d1, d2, l, cl in zip(dof1, dof2, ltype, col):
        #generate 10000 random variables for given degrees of freedom
        x = stats.f.rvs(dfn = d1, dfd = d2, size = 10000)
        x1 = np.sort(x)
        plt.plot(x1, stats.f.pdf(x1, dfn = d1, dfd = d2), 
                 ls = l, c = cl, label = r'$\nu_1=%.1f,\nu_2=%.1f$' % (d1,d2))  
        
    plt.xlim(0, 5)
    plt.ylim(0, 0.8)
    plt.xlabel('$x$')
    plt.ylabel(r'$f(x|\nu_1,\nu_2)$')
    plt.title('F distribution curve')
    plt.legend(loc='upper right')
    plt.show()

Now play with the function to see the changes on the normal distribution curve.

## change the following values as you wish
## define the input arguments (values of the parameters)
dof1_values = [4, 4, 4, 4]
dof2_values = [4, 10, 20, 30]
linestyles = ['-', '--', '-.', ':']
colors = ['blue', 'green','red','black']

f_curve = f_dist_curve(dof1 = dof1_values, dof2 = dof2_values, ltype = linestyles, col = colors)

display(f_curve)

None

Calculate probabilities associated with chi-square distribution with ‘stats.f.cdf(x, dfn, dfd)’.

Spring 2022

F distribution

F distribution¶