To tell which group means are different, we must look at which pairwise comparisons have stars ( ***) next to them.įrom the table we can see that the following group means are statistically significantly different: 5163 inches and 1.6170 inches.įirst, we’ll look at the Tukey post-hoc comparisons for sunlight: This means we’re 95% confident that the true difference in mean height between plants watered daily and plants watered weekly is between. The 95% confidence interval for the difference in mean height is. Next, we can look at the results of the Tukey post-hoc tests to determine which levels of water and sunlight are statistically significantly different.įirst, we’ll look at the Tukey post-hoc comparisons for water:įrom the output we can see that the mean difference in height between plants that were watered daily vs. This tells us that both water and sunlight are statistically significant predictors of plant height and that there is no statistically significant interaction effect between water and sunlight. The p-value for the interaction between water and sunlight.The first table we want to analyze in the results is the ANOVA table: Model height = water sunlight water*sunlight Next, we’ll use proc ANOVA to perform the two-way ANOVA: /*perform two-way ANOVA*/ We can use the following code to create this dataset in SAS: /*create dataset*/ After one month, she records the height of each plant. She plants 30 seeds and lets them grow for one month under different conditions for sunlight exposure and watering frequency.
Suppose a botanist wants to know whether or not plant growth is influenced by sunlight exposure and watering frequency.
Two way anova in jmp how to#
This tutorial provides a step-by-step example of how to perform a two-way ANOVA in SAS.
A two-way ANOVA is used to determine whether or not there is a statistically significant difference between the means of three or more independent groups that have been split on two variables (sometimes called “factors”).
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