4

I suspect that a major problem is the time-series nature of the data. As the Wikipedia page says, inference with linear regression assumes that "errors of the response variables [around the values predicted by the linear model] are uncorrelated with each other." That is often not the case with time series, for which the errors around the predicted values ...


3

There is a confounder: The Earth's position on its orbit around the Sun determines on one hand the season (and, consequently, the ground temperature) and, on the other hand, the amount of light the Moon is receiving. There are subtle traces in the data. The Sun-Earth-Moon angle and the Moon brightness are tightly related, which is not surprising. The Moon ...


1

Usually, you would not care about both of them simultaneously. Depending on the goal of your analysis (say, description vs. prediction), you would only care about one. For description, multicollinearity is just a fact to be mentioned, just one of the characteristics of the data. For prediction, omitted variable bias is largely irrelevant as you are not ...


1

To be honest, I'd be approaching this from the view of the theory I was exploring, rather than just hunting through the data for something interesting. I'd certainly expect EYE_WIDTH_MAX and EYE_WIDTH_AVG to be very highly correlated. So I would have decided in advance which to consider. Secondly, I apologise if I'm missing some context here, but why ...


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