A quality engineer at a consumer electronics company wants to know whether the defects per television set are from a Poisson distribution. The engineer randomly selects 300 televisions and records the number of defects per television.
- Open the sample data, TelevisionDefects.MTW.
- Choose .
- In Variable, enter Defects.
- In Frequency variable: (optional), enter Observed.
- Click OK.
Interpret the results
The null hypothesis states that the data follow a Poisson distribution. Because the p-value is 0.000, which is less than the significance level of 0.05, the engineer rejects the null hypothesis and concludes that the data do not follow a Poisson distribution. The graphs indicate that the difference between the observed and expected values is large for categories 1 and 
3, and that category 3 is the highest contributor to the chi-square statistic.
Poisson Goodness-of-Fit Test: Defects
Method
Frequencies in Observed
Descriptive Statistics
N Mean
300 0.536667
Observed and Expected Counts for Defects
Poisson Observed Expected Contribution
Defects Probability Count Count to Chi-Square
0 0.584694 213 175.408 8.056
1 0.313786 41 94.136 29.993
2 0.084199 18 25.260 2.086
>=3 0.017321 28 5.196 100.072
Chi-Square Test
Null hypothesis H₀: Data follow a Poisson distribution
Alternative hypothesis H₁: Data do not follow a Poisson distribution
DF Chi-Square P-Value 2 140.208 0.000
