Before analysing your own data, it can be helpful to play with the demonstration data provided with SOFA Statistics. Click the “Enter/Edit Data” button to get started.
This brings up the data selection dialog. Here you can look at existing data tables or make new ones. Here we just want to look at the demonstration data table “demo_tbl”. Click on “Open”.
Here you can see the data we will be test analysing using SOFA Statistics. Note the pale blue column - the background colour indicates the field is read-only. Typically, read-only fields are autonumbered or timestamps.
Click on “Close” when you're finished looking.
On the main SOFA form, click on “Report Tables”,
Let's start with a simple report table of Age Group vs Country. NB all of this data is fictitious and designed to allow features of the program to be demonstrated.
Under the “Columns” label click on “Add” and add Country.
In the demonstration pane below you will see a rough illustration of what the table will look like. If you want to see the actual table, click on “Run”.
If “Add to report” is ticked, the output will also be saved to the end of the output file specified at the bottom of the form.
Next you may want to configure the rows and/or columns. Let's add a total column and columns for row and column percentages.
If you click “Run” with “Add to report” ticked, you can view the result by clicking on the “View” button. This will open your default web browser so you can see the output.
The styling of your table can also be changed - here are some examples of different report tables:
Documentation on making report tables is extended in Making Report Tables
Click on the “Statistics” button on the main SOFA form.
Then click on the “CONFIGURE TEST” button (ANOVA should already be selected).
Let's look at whether there is a difference between the average ages in the 3 different countries. NB all the data here is fictitious and only for example purposes.
In this case, there is probably a real difference (p has a vary small value). Looking at the mean age for each group and the distribution for each group will help us decide how important the difference is for the purpose at hand. NB a difference can be statistically significant and clinically/politically/practically etc insignificant.