Good afternoon! I am performing multiple linear regression within SAS Enterprise Guide (latest version) and I have encountered some strange results. I have ten indicators that all have ten observations each and these have been normalised. I am trying to use one dependent variable and nine predictors. However, whenever I attempt this using either code or the inbuilt wizard, I do not receive any values for Standard Error, t value or Pr >|t|, as shown below. I am using the following code: PROC REG DATA= 'D:\Datasets\PODS\Assignment\Raw Data\GS®\SAS LIB\argentina';
MODEL ExportsGS = ExportCost ImportCost ImportGS Trade Arrivals Revenue Contibution ServiceExp TechExp;
RUN; Interestingly, if I remove a single predictor variable (it doesn't seem to be relevant which one I drop) then I am given the results. These are the results when I drop trade: These results are interesting because although the adjusted R² seems to suggest that the model can account for 99% of the variance in the dependent variable, NONE of the individual variables appear to be statistically significant. I have created a scatter plot to visualise the relationships of the data, and this was as follows: Using this scatterplot I decided to drop Trade and ImportCost and run the regression again and again got confusing results (high R² with Pr > |t| values such as '0.97', etc.,). Interestingly, if I drop one of the strongly correlated predictors, like ImportGS, but decide to include the weakly correlated predictors, like Trade or ImportCost, my R² = 1.0, which seems statistically unlikely. I am clearly doing something wrong in my analysis; can anyone help me get to the bottom of this issue?
... View more
