Normal distribution:
If the random variable x follows a normal distribution with mathematical expectation μ and variance σ2 , denoted as N(μ,σ), then it is possible that the random variable x is distributed as N(μ,σ).
where the expected value determines the location of the density function and the standard deviation determines the magnitude of the distribution, the normal distribution when υ = 0, σ = 0 is the standard normal distribution
Judgment methods include drawing graphs/k-s tests
Drawing:
#Import Module import numpy as np import pandas as pd import as plt %matplotlib inline # Construct a set of random data s = ((1000)+10,columns = ['value']) #Drawing scatterplots and histograms fig = (figsize = (10,6)) ax1 = fig.add_subplot(2,1,1) # Subfigure 1 created (, ) () ax2 = fig.add_subplot(2,1,2) # Create subfigure 2 (bins=30,alpha = 0.5,ax = ax2) (kind = 'kde', secondary_y=True,ax = ax2) ()
The results are as follows:
Use the ks test:
# Import scipy modules from scipy import stats """ The kstest method: a KS test with the following parameters: data to be tested, test method (here set to norm normal distribution), mean and standard deviation The result returns two values: statistic → D value, pvalue → P value The p-value is greater than 0.05, for normal distribution H0: the sample meets H1: the sample does not conform How p>0.05 accepts H0 ,and vice versa """ u = s['value'].mean() # Calculate the mean std = s['value'].std() # Calculate standard deviation (s['value'], 'norm', (u, std))
The result is KstestResult(statistic=0.01441344628501079, pvalue=0.9855029319675546), with a p-value greater than 0.05 for a positive too distribution
Above is python to determine whether a set of data in line with the details of the normal distribution, more information about python normal distribution please pay attention to my other related articles!