$\begingroup$ This is correct as stated - and an important point to add - but perhaps I should be explicit that this accounting for loss of df with parameters estimated by MLE is only the case if the MLE is performed on the multinomial counts rather than on unbinned data (e.g. if you're trying to test some continuous distribution fit by binning the data, the loss of d.f. in the chi-squared ...

Mar 4, 2019 · This is a complicated question because different fields conceive these types of variables differently, where others make no distinction whatsoever (which is the case for many social sciences fields and subfields).

If p is the probability of winning a single game, and N is the number of games one needs to win the series, then the probability "P" of winning the series is given by:

$\begingroup$ The classic use of the z-score is when one assumes that underlying data is normally distributed. One needs mean and std dev to describe a distribution.

Mar 12, 2016 · There are a few ways that adjustments can be done but one of the most common ways when there are multiple variables to adjust for is to simply include, as independent variables into a model, the variables for which you want to adjust.

$\begingroup$ Cross validation (as Nick Sabbe discusses), penalized methods (Dikran Marsupial), or choosing variables based on prior theory (Michelle) are all options.

Jun 22, 2014 · From what I understand, we can only build a regression function that lies within the interval of the training data. For example (only one of the panels is necessary): How would I predict into the

Anyone who follows baseball is familiar with batting averages—simply the number of times a player gets a base hit divided by the number of times he goes up at bat (so it's just a percentage between 0 and 1). .266 is in general considered an average batting average, while .300 is considered an excellent one.

$\begingroup$ @student1 As the consequences of omitting a unit root when it is present are more dangerous than considering the presence of a unit root when the process is actually stationary, we may give preference to have the chance to reject the hypothesis of stationarity when there is a unit root, rather than reject a unit root when the process is stationary.

I have a dataset X which has 10 dimensions, 4 of which are discrete values. In fact, those 4 discrete variables are ordinal, i.e. a higher value implies a higher/better semantic. 2 of these discrete