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  2. Binary data - Wikipedia

    en.wikipedia.org/wiki/Binary_data

    Regression analysis on predicted outcomes that are binary variables is known as binary regression; when binary data is converted to count data and modeled as i.i.d. variables (so they have a binomial distribution), binomial regression can be used.

  3. Binary regression - Wikipedia

    en.wikipedia.org/wiki/Binary_regression

    In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output binary variable. Generally the probability of the two alternatives is modeled, instead of simply outputting a single value, as in linear regression .

  4. ImHex - Wikipedia

    en.wikipedia.org/wiki/ImHex

    ImHex is used by programmers and reverse engineers to view and analyze binary data. [2] ... Custom pattern matching and analysis scripting language; Visual, node ...

  5. Randomness test - Wikipedia

    en.wikipedia.org/wiki/Randomness_test

    A randomness test (or test for randomness), in data evaluation, is a test used to analyze the distribution of a set of data to see whether it can be described as random (patternless). In stochastic modeling , as in some computer simulations , the hoped-for randomness of potential input data can be verified, by a formal test for randomness, to ...

  6. Evaluation of binary classifiers - Wikipedia

    en.wikipedia.org/wiki/Evaluation_of_binary...

    If not known and calculated from data, an accuracy comparison test could be made using "Two-proportion z-test, pooled for Ho: p1 = p2". Not used very much is the complementary statistic, the fraction incorrect (FiC): FC + FiC = 1, or (FP + FN)/(TP + TN + FP + FN) – this is the sum of the antidiagonal , divided by the total population.

  7. Probit model - Wikipedia

    en.wikipedia.org/wiki/Probit_model

    The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories; moreover, classifying observations based on their predicted probabilities is a type of binary classification model. A probit model is a popular specification for a binary response model.

  8. Analysis of algorithms - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_algorithms

    The analysis of the former and the latter algorithm shows that it takes at most log 2 n and n check steps, respectively, for a list of size n. In the depicted example list of size 33, searching for "Morin, Arthur" takes 5 and 28 steps with binary (shown in cyan ) and linear ( magenta ) search, respectively.

  9. Binomial regression - Wikipedia

    en.wikipedia.org/wiki/Binomial_regression

    In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is the number of successes in a series of ⁠ ⁠ independent Bernoulli trials, where each trial has probability of success ⁠ ⁠. [1]