I am stuck on something and think I may have made a big error.
My DV is ticket sales and I have 5 potential IVs: ticket price, income, review score, travel distance, and performance costs. I am trying to build a regression model that best fits the data.
First I performed a correlational analysis to see which IVs are correlated to the DV. This showed that all IVs are correlated to the DV apart from travel distance. So I ran a multiple regression analysis using the enter technique and excluded the predictor travel distance. This then showed that 3/4 predictors contributed to the model, whilst 1 (review score) did not. So I simplified the model by removing the non significant predictor review score.
However, my problem is now that I was messing around with re-running regressions and I entered the predictor that was Not correlated to the DV into the regression (travel distance)- this now shows that it is a significant predictor in the model! And then I went ahead and added in review score, which was not a significant contributor to my previous model. Now it is.
So can we add variables into the multiple regression analysis that were previously shown to not be significantly correlated to the outcome variable? How do you choose what to enter? I thought it was correlation results.. theory would suggest that travel distance should affect tickets bought, but the travel distance is within 20 miles for all participants so not that huge.
Not sure whether to re-do my analysis and results and include all IVs. If I do so, they all seem to be significant in the regression model. How would I make my regression model 'better' in this case?
Thanks