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