Frequencies in Observed |
---|
N | Mean |
---|---|
300 | 0.536667 |
Defects | Poisson Probability | Observed Count | Expected Count | Contribution 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 |
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 |
In these results, the null hypothesis states that the data follow a Poisson distribution. Because the p-value is 0.000, which is less than 0.05, the decision is to reject the null hypothesis. You can conclude that the data do not come from a Poisson distribution.
Use a bar chart of observed and expected values to determine whether, for each category, the number of observed values differs from the number of expected values. Larger differences between observed and expected values indicate that the data do not follow a Poisson distribution.