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Binning means grouping a continuous variable into discrete categories. It is particularly used in reference to histograms, but could also be used more generally in the sense of coarsening.
0
votes
Issues with tritomising and dichotomising variable
If your data is really normally distributed (or as close as can be, given 0-40 count) then it doesn't really split into 3 groups based on the data itself. However, since you say you can visually recog …
8
votes
Benefits of using QQ-plots over histograms
See the work of William S. Cleveland.
Visualizing data is probably the best single source, but also see his web page, especially the bibliography and the page for Visualizing Data (including S+ cod …
2
votes
How to bin a quantitative covariate for multiple regression?
Binning generally results in a loss of information. For more on binning problems see Vanderbilt U site . The only problem will be "no lines present" but that perhaps ought to be a different variable. …
1
vote
Accepted
Is it justified to discretize / bin a skewed variable in a classification problem?
If you are using trees then the algorithm will select the bins for you, regardless of whether the variable is skewed or normal or whatever. There is no need for you to "pre-bin" and such an approach c …
3
votes
Multinomial logistic regression, weighted logistic regression?
I agree with @kjetil that binning is a bad idea.
If the reason for binning is "legitimate 0 values" then you can consider methods for zero inflation. …
1
vote
How to make SalePrice as a discrete value?
Binning the DV will increase both type I and type II error. It also invokes a kind of "magical thinking" - that something magical happens at the breaks. …
1
vote
Accepted
Does taking the ratio of Empirical Distributions (histogram bins) show their differences?
You can certainly say something about them, but I don't know what you can say about the behavior generating each, at least, not from the histograms alone.
But a little logic and knowledge of the field …