Chi Square Test is used to test proportions of > 2 sample data.
Following is a case study to illustrate it’s application.
The process engineer would like to know if different raw material ( type A, B & C ) give significant impact on the Yield. He run the experiment for 3 material types on similar production line. Below is the Yield data collected.

Material A 
Material B 
Material C 
Passed 
95 
93 
96 
Failed 
5 
7 
4 
Total inspected 
100 
100 
100 
So, the practical question here was “Was there significant difference in Yield among the 3 material type?”
It was then translated to statistical question by means of Hypothesis Statement.
Ho : Null Hypothesis => p_{A }= p_{B} = p_{C}
Ha : Alternate Hypothesis => At least one p is different than another.
Next, conduct Statistical Analysis. In Minitab, click Stat>Tables>Chi Square Test ( Two way table in one worksheet ) and select relevant information.
Click OK to view the analysis result from Minitab Session window below.
ChiSquare Test: A, B, C
Expected counts are printed below observed counts
ChiSquare contributions are printed below expected counts
A B C Total
1 95 93 96 284
94.67 94.67 94.67
0.001 0.029 0.019
2 5 7 4 16
5.33 5.33 5.33
0.021 0.521 0.333
Total 100 100 100 300
ChiSq = 0.924, DF = 2, PValue = 0.630
The Pvalue was 0.630. Fail to reject Ho (assuming Alpha risk of 5% i.e. 0.05 )
Statistically, it was to say the material type did not have association with Yield.
Practically, it was concluded that No significant difference in Yield among the 3 material type.