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I'm measuring the garbage output of various cities in my country. I have 5 independent variables as predictors measured at 4 time points.

I'm not interested in the effects of time. I just want to know the regression coefficients for each of my variables. To find that out, I could do 4 separate multiple regressions, one for each time point. But that wouldn't summarize the data well, since I would have 4 sets of results. So my question is, is there a way to enter the data for all four time points and get one set of regression coefficients?

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Are the time points relevant (like time of day) or are they just multiple samples? If they are just multiple samples, you might just take the mean over time points, then run a single regression. You should not run 4 separate regressions (what would that tell you, anyway?). If the time points are relevant (I mean, they carry information, regardless of if they're relevant to you), enter it as another predictor, such that you have y ~ x1 + x2 + ... + x_time

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  • $\begingroup$ Thank you for your answer. I'm not sure what you mean by time points being relevant. They represent the same type of measured data but in different years (2012, 2013...). Does that make them relevant? Also, wouldn't putting in all my data from all years into the same model mess up the assumption of independence of observations, even if I put in time as a predictor? $\endgroup$
    – Borna
    Commented Oct 12, 2017 at 23:22
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This depends on the type of data set. Categorical data or continuous data. For each of the independent variable you can take the average. Then you will get a single value for each independent variable. Apply a multiple linear regression. Again, this is purely depends on your data type. In this case, Assuming that it is continuous variable data.

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