Free reference · 596 entries
The words, before the course.
Short, plain definitions of the terms that turn up in statistics and data science — what they mean, and how they relate to each other. Nothing to sign in for.
18 of 596 entries under B
- Backward EliminationBackward elimination is one of several computer-based iterative variable-selection procedures.
- Bag-of-wordsBag-of-words is a simplified natural language processing concept. Text documents are parsed and output as collections of words (i.e.
- BaggingIn predictive modeling, bagging is an ensemble method that uses bootstrap replicates of the original training data to fit predictive models.
- BanditsBandits refers to a class of algorithms in which users or subjects make repeated choices among, or decisions in reaction to, multiple alternatives.
- Bayes’ TheoremBayes theorem is a formula for revising a priori probabilities after receiving new information.
- Bernoulli DistributionA random variable x has a Bernoulli distribution with parameter 0 < p < 1 if 1–p, x=0 p, x=1 0, x à{0, 1} where P(A) is the probability of outcome A.
- Bernoulli Distribution (Graphical)A random variable x has a Bernoulli distribution with parameter 0 < p < 1 if where P(A) is the probability of outcome A.
- Beta Distribution (Graphical)Suppose x1, x2, … , xn are n independent values of a random variable uniformly distributed within the interval [0,1].
- BiasA general statistical term meaning a systematic (not random) deviation of an estimate from the true value.
- Biased EstimatorAn estimator is a biased estimator if its expected value is not equal to the value of the population parameter being estimated.
- BimodalBimodal literally means “two modes” and is typically used to describe distributions of values that have two centers.
- Binomial DistributionUsed to describe an experiment, event, or process for which the probability of success is the same for each trial and each trial has only two possible outcomes.
- Bonferroni AdjustmentBonferroni adjustment is used in multiple comparison procedures to calculate an adjusted probability a of comparison-wise type I error from the desired probability aFW0 of family-wise type I error.
- Bonferroni Adjustment (Graphical)Bonferroni adjustment is used in multiple comparison procedures to calculate an adjusted probability of comparison-wise type I error from the desired probability of family-wise type I error.
- BoostingIn predictive modeling, boosting is an iterative ensemble method that starts out by applying a classification algorithm and generating classifications.
- BootstrappingBootstrapping is sampling with replacement from observed data to estimate the variability in a statistic of interest.
- Box PlotA box plot is a graph that characterizes the pattern of variation of the data. The plot simultaneously displays several measures of central tendency and dispersion of the data at hand.
- Box’s MBox’s M is a statistic which tests the homoscedasticity assumption in MANOVA – that is the assumption that all covariances are the same for any category.
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