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How to compare within group changes between groups?
Comparing within-group changes between groups is a special situation, but one that comes up very frequently in analyzing data from clinical trials. Suppose you’re testing several arthritis drugs against a placebo, and your efficacy variable is the subject’s reported pain level on a 0-to-10 scale.
Why do we want to compare two population variances?
In this section, we will present how to compare two population variances. Why would we want to compare two population variances? There are many situations, such as in quality control problems, where you may want to choose the process with smaller variability for a variable of interest.
How to compare two groups with multiple measurements?
In each group there are 3 people and some variable were measured with 3-4 repeats. As you can see there are two groups made of few individuals for which few repeated measurements were made. I would like to compare two groups using means calculated for individuals, not measure simple mean for the whole group.
How to compare two population variances in MINITAB?
In Minitab… Choose Stat > Basic Statistics > 2 Variances and complete the dialog boxes. In the dialog box, check ‘Use test and confidence intervals based on normal distribution’ when we are confident the two samples come from a normal distribution. Minitab will compare the two variances using the popular F-test method.
How to compare percentage change between groups over multiple time points?
In that way you can compare the percentage change between groups. This is a great solution, but only holds for pre-post designs. My design has 4 repeated measures. Should I do three ANCOVA’s then (comparing T2 with T1 as covariate, comparing T3 with T2 as covariate and comparing T4 with T3 as covariate)?
How to compare percentage change with percentage change?
Afterwise you can report percentage change by dividing the (mean post-value of the group adjusted for the pre-values – mean pre-value of the group)/ (mean pre-value of the group)*100. In that way you can compare the percentage change between groups. This is a great solution, but only holds for pre-post designs.
When is the mean of one group lower than the other?
Let´s say baseline, 2 months and 5 months after baseline. Now I would like to draw a conclusion if the mean of one group is lower than the other group over time. They most likely weigh the same at the first measurement, with little variation.