# Interpreting a weak model proceeding from there

I originally posted this question and since then I've posted another more specific branch-off question.

I've followed the advice I received in response to my questions, and I've done some statistical analysis. Now I need help analyzing the results and determining my next move. Here's my original explanation of the situation:

My data is a list of records, each one representing an educational seminar event. I have a continuous variable that represents the revenue brought in by each seminar, which is the response variable in my regression. I also have a number of categorical variables which are acting as factors/IVs.

To add a little more detail, these factors include things like day of the week the seminar was held, topic, speaker, etc.

My main goal is to build a model that A) can help to explain what factors most influence revenue and how, and B) has some predictive power.

I performed a multiple regression in R, but much to my dismay, the adjusted $R^2$ value was a mere 0.2188. I know this doesn't mean the factors have no predictive power at all, but I'm wary of making any major strategic decisions based on such a poorly fit model--am I right to feel that way?

I also performed an ANOVA test, and certain factors seemed to be labelled as more significant than others.

My questions are:

1. How should I proceed given the weak fit of my model? Could it be that the data simply isn't helpful and that either random chance or factors I haven't considered are at play here?

2. Though ANOVA tells me which factors appear significant, it doesn't give me any idea of how they are significant. How can I determine what effect each significant factor has from a practical standpoint?

3. Are there any other tests that would help me understand how all these variables are related?

Bear in mind that I am new to R and rusty on statistical methods (I took an intro-level course a few years ago).

4. Now you need to estimate accuracy of the model build on the previous steps. $R^2$ is one of the ways although not very illustrative. ANOVA accounts only for linear dependence. Another and more general way is to perform cross validation and calculate mean error of prediction.