Find definitions and interpretation guidance for every statistic
in the Analysis of variance 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 the coefficients. The total DF is 1 less than the number of rows in the data. The DF for a term shows how many coefficients that term uses. Increasing the number of terms in your model adds more coefficients to the model, which decreases the DF for error. The DF for error are the remaining degrees of freedom that are not used in the model.

For a 2-level factorial design or a Plackett-Burman design, if a design has center points, then one DF is for the test for curvature. If the term for center points is in the model, the row for curvature is part of the model. If the term for center points is not in the model, the row for curvature is part of the error that is used to test terms that are in the model. In response surface and definitive screening designs, you can estimate square terms, so the test for curvature is unnecessary.

Each term in the ANOVA table has a chi-square value. 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 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 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.

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. 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 chi-square value 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. 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 ANOVA 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}.