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In statistics, probability theory, and information theory, a statistical distance quantifies the distance between two statistical objects, which can be two random variables, or two probability distributions or samples, or the distance can be between an individual sample point and a population or a wider sample of points.
The concept of statistical distance is useful when we want to identify objects that are in some sense close to each other. Such measures of distance are useful in classic statistical techniques such as clus-tering and classification, where we attempt to categorize data into distinct classes.
Distance measures can be calculated among plots (aka sample units; the rows in your data matrix) or species (aka variables; the columns in your data matrix). However, most analyses are based on the distances among plots, so that’s what I’ll assume throughout these notes.
Today we will discuss distances and metrics between distributions that are useful in statistics. We will discuss them in two contexts: 1. There are metrics that are analytically useful in a variety of statistical problems, i.e. they have intimate connections with estimation and testing. 2.
5 wrz 2019 · Chi-squared test: a goodness-of-fit test to decide how well a frequency distribution differs from an expected frequency distribution. What I would like to do is compare how much the actual usage durations (blue) differ from ideal usage times (orange) in distribution.
1 sty 2014 · Although the Euclidean distance is the most widely used, it is not an appropriate measure when the statistical properties of variables (attributes of the items) are being explicitly considered because it assumes that the variables are uncorrelated.
1 lut 2021 · We start with the most common distance measure, namely Euclidean distance. It is a distance measure that best can be explained as the length of a segment connecting two points. The formula is rather straightforward as the distance is calculated from the cartesian coordinates of the points using the Pythagorean theorem.
