# Questions tagged [binning]

Binning means grouping a continuous variable into discrete categories. It is particularly used in reference to histograms

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### Knuth rule for number of bins of a histogram vs. chi2 fitting

I try to make a histogram and then fit some distribution to it by means of chi2. The Knuth rule (I have some bimodal cases so I'm not using Freedman-Diaconis or Scott) gives me the following histogram ...
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### When to use equal frequency binning and when equal width binning?

When transforming numerical variables into categorical variables I'm not aware of when should I use equal frequency binning and when equal width binning. Seems that each of them has their own ...
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### Predicting binary outcomes for observations given statistics on binned data

SAT Verbal scores range from 200 to 800 in increments of 10. MIT says that for the class of 2023, the acceptance rates were, for various score ranges 750-800 10% = 677/6504 700-740 06% = 312/5039 ...
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### Using Binning before Mann-Whitney for Temperature Data

I have daily temperature for 2 cities and I am trying to see if we can conclude that one city is warmer than the other. I could use a Mann-Whitney for a whole year or I can bin the temperature into ...
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### How to fill the bins?

Consider 1 million people earning money, sorted in increasing order. The kth decile, i.e. the kth 100,000 of them has an income share of $f(k)$ with $f(k)<f(k+1)$ and $\sum_{k=1}^{10} f(k)=1$. Let ...
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### How to model gender specific values/variables as a predictor variable in the regression model?

My research question is to check whether the Body fat is associated with Hypertension onset. I am using Body fat as a categorical variable (i.e according to the value of body fat, the person will be ...
364 views

### What are easy steps of finding cutpoint in continuous variable with Time to event outcome, in Stata?

I find it painful to manually guess a dichotomized cutpoint predictor (continuous) for an time to event outcome in Simple Cox proportional hazard model. Currently I was trying to find the cutpoint ...
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### How to treat low frequency continuous variable in machine leanring

Hello I am working on machine learning model for count data, and I have various features that are highly skewed. The frequency table for one of the feature is given below. ...
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### Optimal multivariate binning where the cut-points must be the same for all observations

I have a large data set with many discrete and continuous variables. All the variables are present in every observation. I want to explain (the log of) one continuous variable using all the other ...
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### A data-independant transformation to discretize a range of values non-uniformly

I am sure this is trivial, but I am looking for a transformation that nonuniformly discretizes all values of a range into several bins. The bins should be variant and I'd like them to be smaller ...
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### How to calculate errors for cumulative distribution function

I have some data points of the form $(x_i,y_i,\delta y_i)$, where $y$ are counts and the error associated to each $y_i = N$ is $y_i = \sqrt{N}$. I want to create the cumulative distribution of these ...
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### Logistic regression interpretation in SPSS statistics

I observed a very strange behavior while doing logistic regression, univariance analysis and correlation analysis. I have dependent binary variable and several independent variables that should be ...
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### Creating a Predictive Model with Binned Data

I have a health dataset with the number of drinks per month someone consumes, and many other variables that are binned. For example, 1: income less than \$10000, 2=income less than \$20000, and so on. ...
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### Logarithmic binning and log-normal distribution

I've an Italian cities dataset. It's similar to those British ones used in literature, but has some differences, though. I decided to perform a logarithmic binning to avoid noise on the right end of ...
211 views

### How to fit a distribution to binned values that come from administrative data?

Fitting a distribution to data (e.g. with maximum likelihood), or testing goodness of fit (e.g. with Kolmogorov-Smirnov) assumes that the data are randomly drawn from a population. But what if the ...
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### How creating bins for a numeric feature can enables the model to learn nonlinear relationships within a single feature?

I understood How binning of numerical feature would help build correlations between the feature & the predictor. For example For a regression problem, we can bucketize "population" feature into ...
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### Converting a continuous variable to categorical

I have several continuous predictor variables and one binary outcome variable. One of these predictor variables has the following description using the Hmisc package: ...
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### Splitting range of independent variable to maximize prediction within the subranges

I have a dataset with two independent variables $X,Z \in \mathbb{R}$ and a dependent variable $Y \in \mathbb{R}$. This dataset has the following characteristics: given some number $z$ and a "small" \$...
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### Is there a general/golden rule for appropriate binning in a histogram?

I was wondering, is there a general rule or a "golden rule" that sets the appropriate bin size as a function of statistical parameters such as sample size, mean, median, mode, standard deviation, etc. ...