Python for MBA's: Chisqaure test

Chi sqaure test using Google Colab

Independent test

Problem 1:

Perform the chi-square test for the following values

14,16,12,15,17

coding

import numpy as np

 

from scipy.stats import chisquare

 

chisquare([14,16,12,15,17])

 

output

Power_divergenceResult(statistic=1.0, 

pvalue=0.9097959895689501)

 

R for MBA's- Chi sqaure test(Independent)

R for MBA's - Chi sqaure test (independent)

coding

x<-(1567,1233,1456,1678,1456,1111,1895)

> chisq.test(x)

Output

 Chi-squared test for given probabilities

data:  x

X-squared = 280.87, df = 6, p-value < 2.2e-16

Advanced excel for MBA's- 't' test

Learn SPSS- Chi-Sqaure Tests

Python for MBA's- One sample 't' test

T test

This is the most common hypothesis testing statistical test used in social science.
Today we will learn how to calculate One sample 't ' test using Python.

Problem:

Let us take Virat Kohli's  four  innings score in 2020 in New zealand in T 20 matches
They are
11,
38,
11,
45
His career average in T20 Internationals is 50.80
Now the hypothesis is

Virat Kohli's average in T20's in New Zealand 2020 series  significantly differs from career T 20 International average.

The Python code is given below.

import numpy as np

from scipy.stats import norm

from scipy import stats

Virat_t20I=(11,38,11,45)

# Perform t-test and print result

t_result=stats.ttest_1samp(Virat_t20I, 50.8)

print(t_result)

# Test significance

alpha= 0.05

if (t_result[1] < alpha):

    print("mean value of Virat_T20I differs from given value")

else:

  print("No significant difference found")

 

Solution:

Ttest_1sampResult(statistic=-2.7523096064356434, pvalue=0.07060545196814848)
No significant difference found

 

 

 

Python for MBA's- Correlation using Python

Method 1: Using Numpy Library

import numpy as np
advt=[5,10,15,20,25]

sales=[40,60,80,100,120]

a=np.corrcoef(advt,sales)

print(a)

Ans:

[[1. 1.]
 [1. 1.]]

Method 2: Using Scipy library

Problem2: Find the correlation between share volume of  Ajit 

industries and market volume of a sensex. The data is given below:

share volume : 25,35,28,22,34,32

Market volume: 35,55,42,36,45,41

Solution:

Coding:

import numpy as np

from scipy.stats import pearsonr

sharevolume=[25,35,28,22,34,32]

marketvolume=[35,55,42,36,45,41]

pearsonr(sharevolume,marketvolume)

Output

(0.8440172720849535, 0.034598339542112846)