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Generally, it is recommended to drop one variable from modelling when we found any collinearity among two variables. Recommended by whom exactly? Collinearity between explanatory variables is a …

Ordinarily, when we fit data to a proposed model, we test whether the model is "adequate" by looking at the distribution of the residual values, and comparing aspects of this distribution to the theor …

Without observing actual data from those sports, what you are talking about are essentially just prior beliefs about what the processes might plausibly look like. Ultimately you will need to test the …

The problem with Poisson models is that they are single-parameter models that do not allow a free variance parameter. For this reason, when dealing with count data it is usually far better to use a n …

I recommend you start by looking at statistical/econometric literature on "identifiability". That concept pertains to reducdancy of parameters in a model, but I suppose it could also accomodate redun …

Yes, it does bias the p-values Your intuition on this is correct --- generally speaking, whenever we select a model via optimisation we bias the resulting p-value for any tests that fail to account fo …

This can be framed as a problem with censoring if you like. Given that it is possible to take a sample without finding the disease (even if it is there) you have two aspects to your inference problem …

Why do we worry about overfitting even if “all models are wrong”? Your question appears to be a variation of the Nirvana fallacy, implicitly suggesting that if there is no perfect model, then eve …

The term "machine learning" is somewhat a term of art, but it generally refers to the construction of algorithms that "learn through experience". The requirement of learning through experience necess …

It is not really possible to say which type of model will fit the data best prior to doing some actual model-fitting and model-comparison. However, I can give you some advice on where I'd start. Obv …

From the representation theorem, we know that exchangeability is essentially just an operational condition that is equivalent to the conditional IID form (which implies equicorrelation among the obser …

Let $p$ be the price of the item and let $Q$ be the corresponding indicator of a sale (your quantity variable). Then your logistic regression can be specified as: $$\mathbb{E}(Q) = \text{logistic}(\ …

I think the simple answer here is that we usually implicitly form a view on a space of possible events when we form a probability model. Typically we will demarcate the model to allow a set of outcom …

There are two general ways you could go about doing this: Build a single model of both datasets: If you can build a single model that incorporates the data from both analyses then this will naturally …

In order to build a model of this kind, it is important to first understand some basic statistical aspects of discrimination and process-outcomes. This requires understanding of statistical processes …