Correlation analysis of Nominal data with Chi-Square Test in Data Mining

Correlation analysis of Nominal data with Chi-Square Test in Data Mining

Chi-Square Test

This analysis can be done by chi-square test.Chi-square test is the test to analyze the correlation of nominal data.

Correlation VS Causality:

Correlation does not always tell us about causality.

Example:

  • The number of students passed in exam and number of car theft in a country is correlated with each other but maybe it does not mean that number of student passed effects car theft in a country.

But in some cases it may be;

  • The number of students passed in exam and number of students who live near to the university is correlated with each other and maybe a number of students who live near to the university can be a cause of the student result.
[quads id=2]
  Passed student Not passed student Sum
Live near University Observed=140

Expected = 180*330/1320

Expected =45

Observed=190

Expected = 1140*330/1320

Expected =285

330
Not live near University Observed=40

Expected = 180*990/1320

Expected =135

Observed=950

Expected = 1140*990/1320

Expected =855

990
Sum 140 + 40 = 180 190 + 950 = 1140 1320

correlation analysis

Degrees of freedom:

DF = (r – 1) * (c – 1)

Level of significance:

.01 .05 .10

 

Fazal Rehman Shamil
Welcome to all friends. The reason for our success is only your love for T4Tutorials. Our team is always available to answer your queries regarding any kind of confusions or discussion regarding your study and career matters. For discussion with us please join our facebook group "T4Tutorials.com". The link of the group is mentioned below. Thanks and love to all for connecting with us. We are nothing without you. Love you all.....
https://web.facebook.com/groups/2066136233601097/

Leave a Reply