You are building a web site and are A/B testing two different designs for the site. You want to know which designs lead to people taking a certain action on your web site (e.g. buying a product, clicking a button). The tests for the different designs are conducted separately over time because you do not want there to be two different versions of the web site available at any given time.

You are building a web site and are A/B testing two different designs for the site. You want to know which designs lead to people taking a certain action on your web site (e.g. buying a product, clicking a button). The tests for the different designs are conducted separately over time because you do not want there to be two different versions of the web site available at any given time. Therefore, one issue may be the presence of time trends that can confound the relationship between a given design and the number of actions taken. The target of estimation is the average difference in actions taken on the web site per visit between design A and design B.

In your formal modeling you fit a series of models to account for possible temporal trends in different ways. The results of your primary model and 4 secondary models is are shown below.

What could you conclude from these results?