The degrees of freedom (DF) are the amount of information in your data. The analysis uses that information for the F tests for testing fixed effect terms. The DF Num displays the numerator degrees of freedom for the F test for a fixed effect term. The value equals the number of parameters for the fixed effect term. The DF Den displays the denominator degrees of freedom for the F test for a fixed effect term.
An F-value appears for each fixed effect term in the Tests of Fixed Effects table. The F-value is for the F-test that determines whether the term significantly affects the response.
Minitab uses the F-value to calculate the p-value, which you use to make a decision about the statistical significance of the term. 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 F-value indicates that the term is significant.
If you want to use the F-value to determine whether to reject the null hypothesis, compare the F-value to your critical value. You can calculate the critical value in Minitab or find the critical value from an F-distribution table in most statistics books. For more information on using Minitab to calculate the critical value, go to Using the inverse cumulative distribution function (ICDF) and click "Use the ICDF to calculate critical values".
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 a term significantly affects the response, compare the p-value to your significance level. 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 affect exists when there is no actual affect.
The interpretation of each p-value depends on whether it is for the coefficient of a fixed factor term or for a covariate term.
If the p-value is less than or equal to the significance level, you can conclude that the fixed factor term does significantly affect the response. The rejection of the null hypothesis means one level effect is significantly different from the other level effects of the term.