i need some help. I hope you can guide me.

I have a survey and i need to predict the variable e18.

The issue is i'm not sure how to treat the age variable.

One alternative is using it directly into the regression model.

The other, more interesting, is to transform it into a discrete variable.

The range it would be:

0-15;16-20;20-25;25-30;30-35;35-40;45-50;50-55;55-60;60-65;65 and more.

So, between those model, one with age incorporated directly in the equation and the other computed it as a discrete variable, what's the criteria for choosing the best model predicting?

Only R square? AIC, BIC? t-statistics?

In principle, should it be the continuous the best model adjusting data?

Thanks for your time and interest. ]]>

I want to look at how my predictor is associated with each of cognitive outcomes, separately. What would be the best analysis to use here? It’s been suggested that I use logistic regression? Would this allow me to do between group comparisons, or would another analysis be more appropriate?

Finally, given that the data isn’t normal or continuous am I right in assuming that a straightforward ANOVA with Bonferroni correction is out of the question? ]]>

I have not worked so much with this type of regressions and have tried to find the answer in my econometric books and on internet, but have unfortunately not had any luck. I appreciate all help I can get with this!

Im using time series data to measure the rate of return of the inputs Xi on the outputs Yi.

I need to find the values of the three coefficients a1, a2 and b, which are all appering in three related equations:

ln(Y1/Y3)=ln((1-a1+a2)/a2) +b*ln(X1/X3) + e

ln(Y2/Y3)=ln((1-a1)/a2)) +b*ln(X2/X3) + e

ln(Y1/Y2)=ln((a1+a2)/a1) +b*ln(X1/X2) +e

where X1, X2 and X3 are the independent variables and e the error term.

Is it correct to estimate this using OLS and then solve the equation system for a1 and a2? Or will the estimates be biased?

I only want one value of b, can I get that using this method?

Thank you so much for your time!

/C ]]>