Question

c) Is the F-value significant? State your conclusions. d) Next, re-analyze the data as a Randomized Complete Block Design (RCBD) using Ranch as the block source. Construct and complete an ANOVA table. Use SAS to confirm your results and include print-out. e) Restate your conclusions. Was it effective to block the data for the Ranch source? Explain in sufficient detail. Also, calculate the SEMs for the two designs (CRD and RCBD) using your ANOVA results. Explain why these values are different?

Answer 1

C).   An F statistic is a value you get when you run an ANOVA test or a regression analysis to find out if the means between two populations are significantly different.An F test will tell you if a group of variables are jointly significant.We can use the F statistic when deciding to support or reject the null hypothesis. In our F test results, we will  have both an F value and an F critical value.The value you calculate from your data is called the F value . if your calculated F value in a test is larger than your F statistic, you can reject the null hypothesis.The F statistic must be used in combination with the p value when you are deciding if your overall results are significant.

F value = variance of the group means (Mean Square Between) / mean of the within group variances (Mean Squared Error)

The F value in one way ANOVA is a tool to help you answer the question “Is the variance between the means of two populations significantly different.

The F value should always be used along with the p value in deciding whether your results are significant enough to reject the null hypothesis. If you get a large f value (one that is bigger than the F critical value found in a table), it means something is significant, while a small p value means all your results are significant.