Find definitions and interpretation guidance for every statistic in the Deviance table.

The total degrees of freedom (DF) are the amount of information in your data. The analysis uses that information to estimate the values of unknown population parameters. The total DF is 1 less than the number of rows in the data. The DF for a term show how much information that term uses. Increasing the number of terms in your model uses more information, which decreases the DF for error. The DF for error are the information available to estimate the parameters.

Adjusted deviances are measures of variation for different components of the model. The order of the predictors in the model does not affect the calculation of the adjusted deviances. In the Deviance table, Minitab separates the deviance into different components that describe the deviance from different sources.

- Regression
- The adjusted deviance for the regression model quantifies the difference between the current model and the full model.
- Term
- The adjusted deviance for a term quantifies the difference between a model with that term and the full model.
- Error
- The adjusted deviance for error quantifies the deviance that the model does not explain.
- Total
- The total adjusted deviance is the sum of the adjusted deviance for the model and the adjusted deviance for error. The total adjusted deviance quantifies the total deviance in the data.

Minitab uses the adjusted deviances to calculate the p-value for a term. Minitab also uses the adjusted deviances to calculate the deviance R^{2} statistic. Usually, you interpret the p-values and the R^{2} statistic instead of the deviances.

Adjusted mean deviance measures how much deviance a term or a model explains for each degree of freedom. The calculation of the adjusted mean deviance for each term assumes that all other terms are in the model.

Minitab uses the adjusted mean deviance to calculate the p-value for a term. Usually, you interpret the p-values instead of the adjusted mean squares.

Sequential deviance measures the deviance for different components of the model. Unlike adjusted deviance, the sequential deviance depends on the order that the terms enter the model. In the Deviance table, Minitab separates the sequential deviance into different components that describe the deviance from different sources.

- Regression
- The sequential deviance for the regression model quantifies the difference between the current model and the full model.

- Term
- The sequential deviance for a term quantifies the difference between a model with the term and the full model.
- Error
- The sequential deviance for error quantifies the deviance that the model does not explain.
- Total
- The total sequential deviance is the sum of the sequential deviance for the model and the sequential deviance for error. The total sequential deviance quantifies the total deviance in the data.

When you specify use of the sequential deviance for tests, Minitab uses the sequential deviance to calculate the p-values for the regression model and the individual terms. Usually, you interpret the p-values instead of the sequential deviance.

Sequential mean deviance measures how much deviance a term or model explains for each degree of freedom. The calculation of the sequential mean deviance depends on the order that the terms enter the model.

Minitab uses the sequential mean deviance to calculate the p-value for a term. Usually, you interpret the p-values instead of the sequential mean squares.

Contribution displays the percentage that each source in the Deviance table contributes to the total sequential deviance.

Higher percentages indicate that the source accounts for more of the deviance in the response variable. The percent contribution for the regression model is the same as the deviance R^{2}.

Each term in the Deviance table has a chi-square value for the likelihood ratio test. The chi-square value is the test statistic that determines whether a term or model has an association with the response.

Minitab uses the chi-square statistic to calculate the p-value, which you use to make a decision about the statistical significance of the terms and the model. The p-value is a probability that measures the evidence against the null hypothesis. Lower probabilities provide stronger evidence against the null hypothesis. A sufficiently large chi-square statistic results in a small p-value, which indicates that the term or model is statistically significant.

The p-value is a probability that measures the evidence against the null hypothesis. Lower probabilities provide stronger evidence against the null hypothesis.

To determine whether the data provide evidence that at least one coefficient in the regression model is different from 0, compare the p-value for regression to your significance level to assess the null hypothesis. The null hypothesis for the p-value for regression is that all of the coefficients for terms in the regression model are 0. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that at least one coefficient is different from 0 when all of the coefficients are 0.

- P-value ≤ α: At least one coefficient is different from 0
- If the p-value is less than or equal to the significance level, you conclude that at least one coefficient is different from 0.
- P-value > α: Not enough evidence exists to conclude that at least one coefficient is different from 0
- If the p-value is greater than the significance level, you cannot conclude that at least one coefficient is different from 0. You may want to fit a new model.

The tests in the Deviance table are likelihood ratio tests. The test in the expanded display of the Coefficients table are Wald approximation tests. The likelihood ratio tests are more accurate for small samples than the Wald approximation tests.

The p-value is a probability that measures the evidence against the null hypothesis. Lower probabilities provide stronger evidence against the null hypothesis.

To determine whether the association between the response and each term in the model is statistically significant, compare the p-value for the term to your significance level to assess the null hypothesis. The null hypothesis is that there is no association between the term and the response. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that an association exists when there is no actual association.

- P-value ≤ α: The association is statistically significant
- If the p-value is less than or equal to the significance level, you can conclude that there is a statistically significant association between the response variable and the term.
- P-value > α: The association is not statistically significant
- If the p-value is greater than the significance level, you cannot conclude that there is a statistically significant association between the response variable and the term. You may want to refit the model without the term.
- If there are multiple predictors without a statistically significant association with the response, you can reduce the model by removing terms one at a time. For more information on removing terms from the model, go to Model reduction.

If a model term is significant, the interpretation depends on the type of term. The interpretations are as follows:

- If a continuous predictor is significant, you can conclude that the coefficient for the predictor is different from 0.
- If a categorical predictor is significant, you can conclude that not all the level means are equal.
- If an interaction term is significant, you can conclude that the relationship between the predictor and the response depends on the other predictors in the term.
- If a polynomial term is significant, you can conclude that the relationship between the predictor and the response depends on the magnitude of the predictor.