Chi-Square 2

Data Science Day 4:

Chi-Square test application 1:
Test Goodness of a fit.

We use the goodness of a fit to test if the observed categorical data follows the hypothesized or expected distribution.

Example 1: P-value Interpretation

Suppose f_exp are the expected number of boys in grade 1 different classes. f_obs are the observed number of boys in grade 1. We want to see if f_obs is the same as the f_exp distribution.

H0(Null Hypotheses): the observation boy students distribution is consistent with the expected distribution.

Boy Students Distribution

1810
155
57
818
410
311

We use the following python code to acquire the p-value:

Chisquare(f_obs=[18,15,5,8,4,3], f_exp=[10,5,7,18,10,11])

For this particular example, the p-value=6.02e-08, which is significantly smaller than 0.05. So we reject H0, and conclude the observed boy students distribution is Different from the Expected boy distributions.

 

Example 2: Data visualization Interpretation

We will graph a Chi-square distribution with degree 5 and size 1000, and use Kernel Density Estimation to fit the graph. We can see this is a pretty good fit.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

To be continue…..

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