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Logistic regression, the Glossary

In statistics, the logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables.^[1]

190 relations: Adolphe Quetelet, Analytic function, Autocatalysis, Backpropagation, Bayesian statistics, Bernoulli distribution, Bernoulli trial, Binary classification, Binary data, Binary regression, Binomial distribution, Bioassay, Blood test, Body mass index, Brier score, Built environment, C (programming language), C++, Cambridge University Press, Canada, Canonical form, Cardinal number, Catalysis, Categorical variable, Chester Ittner Bliss, Chi-squared test, Coefficient of determination, Computer science, Conditional entropy, Conditional logistic regression, Conditional random field, Conjugate prior, Contingency table, Continuous or discrete variable, Coronary artery disease, Cross-entropy, Cumulative distribution function, Daniel McFadden, David Cox (statistician), Degrees of freedom (statistics), Dependent and independent variables, Design matrix, Deviance (statistics), Diabetes, Discrete choice, Dot product, Dummy variable (statistics), E (mathematical constant), Econometrics, Economics, ... Expand index (140 more) »
Predictive analytics
Regression models

Adolphe Quetelet

Lambert Adolphe Jacques Quetelet FRSF or FRSE (22 February 1796 – 17 February 1874) was a Belgian astronomer, mathematician, statistician and sociologist who founded and directed the Brussels Observatory and was influential in introducing statistical methods to the social sciences.

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Analytic function

In mathematics, an analytic function is a function that is locally given by a convergent power series.

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Autocatalysis

In chemistry, a chemical reaction is said to be autocatalytic if one of the reaction products is also a catalyst for the same reaction.

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Backpropagation

In machine learning, backpropagation is a gradient estimation method used to train neural network models.

See Logistic regression and Backpropagation

Bayesian statistics

Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event.

See Logistic regression and Bayesian statistics

Bernoulli distribution

In probability theory and statistics, the Bernoulli distribution, named after Swiss mathematician Jacob Bernoulli, is the discrete probability distribution of a random variable which takes the value 1 with probability p and the value 0 with probability q.

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Bernoulli trial

In the theory of probability and statistics, a Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted.

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Binary classification

Binary classification is the task of classifying the elements of a set into one of two groups (each called class).

See Logistic regression and Binary classification

Binary data

Binary data is data whose unit can take on only two possible states.

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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.

See Logistic regression and Binary regression

Binomial distribution

In probability theory and statistics, the binomial distribution with parameters and is the discrete probability distribution of the number of successes in a sequence of independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success (with probability) or failure (with probability).

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Bioassay

A bioassay is an analytical method to determine the potency or effect of a substance by its effect on living animals or plants (in vivo), or on living cells or tissues (in vitro).

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Blood test

A blood test is a laboratory analysis performed on a blood sample that is usually extracted from a vein in the arm using a hypodermic needle, or via fingerprick.

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Body mass index

Body mass index (BMI) is a value derived from the mass (weight) and height of a person.

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Brier score

The Brier Score is a ''strictly proper score function'' or ''strictly proper scoring rule'' that measures the accuracy of probabilistic predictions.

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Built environment

The term built environment refers to human-made conditions and is often used in architecture, landscape architecture, urban planning, public health, sociology, and anthropology, among others.

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C (programming language)

C (pronounced – like the letter c) is a general-purpose programming language.

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C++

C++ (pronounced "C plus plus" and sometimes abbreviated as CPP) is a high-level, general-purpose programming language created by Danish computer scientist Bjarne Stroustrup.

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Cambridge University Press

Cambridge University Press is the university press of the University of Cambridge.

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Canada

Canada is a country in North America.

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Canonical form

In mathematics and computer science, a canonical, normal, or standard form of a mathematical object is a standard way of presenting that object as a mathematical expression.

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Cardinal number

In mathematics, a cardinal number, or cardinal for short, is what is commonly called the number of elements of a set.

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Catalysis

Catalysis is the increase in rate of a chemical reaction due to an added substance known as a catalyst.

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Categorical variable

In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property.

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Chester Ittner Bliss

Chester Ittner Bliss (February 1, 1899 – March 14, 1979) was primarily a biologist, who is best known for his contributions to statistics.

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Chi-squared test

A chi-squared test (also chi-square or test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large.

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Coefficient of determination

In statistics, the coefficient of determination, denoted R2 or r2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

See Logistic regression and Coefficient of determination

Computer science

Computer science is the study of computation, information, and automation.

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Conditional entropy

In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable Y given that the value of another random variable X is known.

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Conditional logistic regression

Conditional logistic regression is an extension of logistic regression that allows one to account for stratification and matching.

See Logistic regression and Conditional logistic regression

Conditional random field

Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction.

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Conjugate prior

In Bayesian probability theory, if, given a likelihood function p(x \mid \theta), the posterior distribution p(\theta \mid x) is in the same probability distribution family as the prior probability distribution p(\theta), the prior and posterior are then called conjugate distributions with respect to that likelihood function and the prior is called a conjugate prior for the likelihood function p(x \mid \theta).

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Contingency table

In statistics, a contingency table (also known as a cross tabulation or crosstab) is a type of table in a matrix format that displays the multivariate frequency distribution of the variables.

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Continuous or discrete variable

In mathematics and statistics, a quantitative variable may be continuous or discrete if they are typically obtained by measuring or counting, respectively.

See Logistic regression and Continuous or discrete variable

Coronary artery disease

Coronary artery disease (CAD), also called coronary heart disease (CHD), ischemic heart disease (IHD), myocardial ischemia, or simply heart disease, involves the reduction of blood flow to the cardiac muscle due to build-up of atherosclerotic plaque in the arteries of the heart.

See Logistic regression and Coronary artery disease

Cross-entropy

In information theory, the cross-entropy between two probability distributions p and q, over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set is optimized for an estimated probability distribution q, rather than the true distribution p.

See Logistic regression and Cross-entropy

Cumulative distribution function

In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x. Every probability distribution supported on the real numbers, discrete or "mixed" as well as continuous, is uniquely identified by a right-continuous monotone increasing function (a càdlàg function) F \colon \mathbb R \rightarrow satisfying \lim_F(x).

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Daniel McFadden

Daniel Little McFadden (born July 29, 1937) is an American econometrician who shared the 2000 Nobel Memorial Prize in Economic Sciences with James Heckman.

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David Cox (statistician)

Sir David Roxbee Cox (15 July 1924 – 18 January 2022) was a British statistician and educator.

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Degrees of freedom (statistics)

In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary.

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Dependent and independent variables

A variable is considered dependent if it depends on an independent variable.

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Design matrix

In statistics and in particular in regression analysis, a design matrix, also known as model matrix or regressor matrix and often denoted by X, is a matrix of values of explanatory variables of a set of objects.

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Deviance (statistics)

In statistics, deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing.

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Diabetes

Diabetes mellitus, often known simply as diabetes, is a group of common endocrine diseases characterized by sustained high blood sugar levels.

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Discrete choice

In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport.

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Dot product

In mathematics, the dot product or scalar productThe term scalar product means literally "product with a scalar as a result".

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Dummy variable (statistics)

In regression analysis, a dummy variable (also known as indicator variable or just dummy) is one that takes a binary value (0 or 1) to indicate the absence or presence of some categorical effect that may be expected to shift the outcome.

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E (mathematical constant)

The number is a mathematical constant approximately equal to 2.71828 that can be characterized in many ways.

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Econometrics

Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships.

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Economics

Economics is a social science that studies the production, distribution, and consumption of goods and services.

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Edwin Bidwell Wilson

Edwin Bidwell Wilson (April 25, 1879 – December 28, 1964) was an American mathematician, statistician, physicist and general polymath.

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Emergency management

Emergency management (also disaster management) is a science and a system charged with creating the framework within which communities reduce vulnerability to hazards and cope with disasters.

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Engineering

Engineering is the practice of using natural science, mathematics, and the engineering design process to solve technical problems, increase efficiency and productivity, and improve systems.

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Entropy (information theory)

In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent to the variable's possible outcomes.

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Error function

In mathematics, the error function (also called the Gauss error function), often denoted by, is a function defined as: \operatorname z.

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Estimation theory

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component.

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Euler numbers

In mathematics, the Euler numbers are a sequence En of integers defined by the Taylor series expansion where \cosh (t) is the hyperbolic cosine function.

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Expectation propagation

Expectation propagation (EP) is a technique in Bayesian machine learning.

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Expected value

In probability theory, the expected value (also called expectation, expectancy, expectation operator, mathematical expectation, mean, expectation value, or first moment) is a generalization of the weighted average.

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Exponential family

In probability and statistics, an exponential family is a parametric set of probability distributions of a certain form, specified below.

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Exponential function

The exponential function is a mathematical function denoted by f(x).

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Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. Logistic regression and generalized linear model are regression models.

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Goodness of fit

The goodness of fit of a statistical model describes how well it fits a set of observations.

See Logistic regression and Goodness of fit

Gradient descent

Gradient descent is a method for unconstrained mathematical optimization.

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Heavy-tailed distribution

In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier tails than the exponential distribution.

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Hosmer–Lemeshow test

The Hosmer–Lemeshow test is a statistical test for goodness of fit and calibration for logistic regression models.

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Identifiability

In statistics, identifiability is a property which a model must satisfy for precise inference to be possible.

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Independence of irrelevant alternatives

Independence of irrelevant alternatives (IIA), also known as binary independence, the independence axiom, is an axiom of decision theory and economics describing a necessary condition for rational behavior.

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Independent and identically distributed random variables

In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent.

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Indicator function

In mathematics, an indicator function or a characteristic function of a subset of a set is a function that maps elements of the subset to one, and all other elements to zero.

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Information content

In information theory, the information content, self-information, surprisal, or Shannon information is a basic quantity derived from the probability of a particular event occurring from a random variable.

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Iteratively reweighted least squares

The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a ''p''-norm: \mathop_ \sum_^n \big| y_i - f_i (\boldsymbol\beta) \big|^p, by an iterative method in which each step involves solving a weighted least squares problem of the form:C.

See Logistic regression and Iteratively reweighted least squares

Jane Worcester

Jane Worcester (died 8 October 1989) was a biostatistician and epidemiologist who became the second tenured female professor, after Martha May Eliot, and the first female chair of biostatistics in the Harvard School of Public Health.

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Jarrow–Turnbull model

The Jarrow–Turnbull model is a widely used "reduced-form" credit risk model.

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John Gaddum

Sir John Henry Gaddum (31 March 1900 – 30 June 1965) was an English pharmacologist who, along with Ulf von Euler, co-discovered the neuropeptide Substance P in 1931.

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Joseph Berkson

Joseph Berkson (14 May 1899 – 12 September 1982) was trained as a physicist (BSc 1920, College of City of New York, M.A., 1922, Columbia), physician (M.D., 1927, Johns Hopkins), and statistician (Dr.Sc., 1928, Johns Hopkins).

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Journal of Clinical Epidemiology

The Journal of Clinical Epidemiology is a peer-reviewed journal of epidemiology.

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Just another Gibbs sampler

Just another Gibbs sampler (JAGS) is a program for simulation from Bayesian hierarchical models using Markov chain Monte Carlo (MCMC), developed by Martyn Plummer.

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Kullback–Leibler divergence

In mathematical statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence), denoted D_\text(P \parallel Q), is a type of statistical distance: a measure of how one probability distribution is different from a second, reference probability distribution.

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L. Gustave du Pasquier

Louis-Gustave du Pasquier (18 August 1876, Auvernier – 31 January 1957, Cornaux) was a Swiss mathematician and historian of mathematics and mathematical sciences.

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Lagrange multiplier

In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables).

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Latent and observable variables

In statistics, latent variables (from Latin: present participle of lateo, “lie hidden”) are variables that can only be inferred indirectly through a mathematical model from other observable variables that can be directly observed or measured.

See Logistic regression and Latent and observable variables

Latent variable model

A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent variables.

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Likelihood function

A likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model.

See Logistic regression and Likelihood function

Likelihood-ratio test

In statistics, the likelihood-ratio test is a hypothesis test that involves comparing the goodness of fit of two competing statistical models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods.

See Logistic regression and Likelihood-ratio test

Limited dependent variable

A limited dependent variable is a variable whose range of possible values is "restricted in some important way." In econometrics, the term is often used when estimation of the relationship between the limited dependent variable of interest and other variables requires methods that take this restriction into account.

See Logistic regression and Limited dependent variable

Limited-memory BFGS

Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory.

See Logistic regression and Limited-memory BFGS

Linear combination

In mathematics, a linear combination is an expression constructed from a set of terms by multiplying each term by a constant and adding the results (e.g. a linear combination of x and y would be any expression of the form ax + by, where a and b are constants).

See Logistic regression and Linear combination

Linear discriminant analysis

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events.

See Logistic regression and Linear discriminant analysis

Linear function (calculus)

In calculus and related areas of mathematics, a linear function from the real numbers to the real numbers is a function whose graph (in Cartesian coordinates) is a non-vertical line in the plane.

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Linear least squares

Linear least squares (LLS) is the least squares approximation of linear functions to data.

See Logistic regression and Linear least squares

Linear model

In statistics, the term linear model refers to any model which assumes linearity in the system. Logistic regression and linear model are regression models.

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Linear predictor function

In statistics and in machine learning, a linear predictor function is a linear function (linear combination) of a set of coefficients and explanatory variables (independent variables), whose value is used to predict the outcome of a dependent variable.

See Logistic regression and Linear predictor function

Linear regression

In statistics, linear regression is a statistical model which estimates the linear relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables).

See Logistic regression and Linear regression

Local case-control sampling

In machine learning, local case-control sampling is an algorithm used to reduce the complexity of training a logistic regression classifier.

See Logistic regression and Local case-control sampling

Location parameter

In statistics, a location parameter of a probability distribution is a scalar- or vector-valued parameter x_0, which determines the "location" or shift of the distribution.

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Logarithm

In mathematics, the logarithm is the inverse function to exponentiation.

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Logistic distribution

In probability theory and statistics, the logistic distribution is a continuous probability distribution.

See Logistic regression and Logistic distribution

Logistic function

A logistic function or logistic curve is a common S-shaped curve (sigmoid curve) with the equation where The logistic function has domain the real numbers, the limit as x \to -\infty is 0, and the limit as x \to +\infty is L. The standard logistic function, depicted at right, where L.

See Logistic regression and Logistic function

Logistic model tree

In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning.

See Logistic regression and Logistic model tree

Logit

In statistics, the logit function is the quantile function associated with the standard logistic distribution.

See Logistic regression and Logit

Loss function

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.

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Loss functions for classification

In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to).

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Lowell Reed

Lowell Jacob Reed (January 8, 1886 – April 29, 1966) was 7th president of the Johns Hopkins University in Baltimore, Maryland.

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Luce's choice axiom

In probability theory, Luce's choice axiom, formulated by R. Duncan Luce (1959), states that the relative odds of selecting one item over another from a pool of many items is not affected by the presence or absence of other items in the pool.

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Machine learning

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions.

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Mark Thoma

Mark Allen Thoma (born December 15, 1956) is a macroeconomist and econometrician and a professor of economics at the Department of Economics of the University of Oregon.

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Marketing

Marketing is the act of satisfying and retaining customers.

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Matching (statistics)

Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).

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Maximum a posteriori estimation

In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution.

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Maximum entropy probability distribution

In statistics and information theory, a maximum entropy probability distribution has entropy that is at least as great as that of all other members of a specified class of probability distributions.

See Logistic regression and Maximum entropy probability distribution

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data.

See Logistic regression and Maximum likelihood estimation

Mixed logit

Mixed logit is a fully general statistical model for examining discrete choices.

See Logistic regression and Mixed logit

Mlpack

mlpack is a machine learning software library for C++, built on top of the Armadillo library and the numerical optimization library.

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Mortgage

A mortgage loan or simply mortgage, in civil law jurisdictions known also as a hypothec loan, is a loan used either by purchasers of real property to raise funds to buy real estate, or by existing property owners to raise funds for any purpose while putting a lien on the property being mortgaged.

See Logistic regression and Mortgage

Multicollinearity

In statistics, multicollinearity or collinearity is a situation where the predictors in a regression model are linearly dependent.

See Logistic regression and Multicollinearity

Multinomial logistic regression

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. Logistic regression and multinomial logistic regression are regression models.

See Logistic regression and Multinomial logistic regression

Myocardial infarction

A myocardial infarction (MI), commonly known as a heart attack, occurs when blood flow decreases or stops in one of the coronary arteries of the heart, causing infarction (tissue death) to the heart muscle.

See Logistic regression and Myocardial infarction

Natural language processing

Natural language processing (NLP) is an interdisciplinary subfield of computer science and artificial intelligence.

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Natural logarithm

The natural logarithm of a number is its logarithm to the base of the mathematical constant e, which is an irrational and transcendental number approximately equal to.

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Neural network (machine learning)

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains.

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Newton's method

In numerical analysis, Newton's method, also known as the Newton–Raphson method, named after Isaac Newton and Joseph Raphson, is a root-finding algorithm which produces successively better approximations to the roots (or zeroes) of a real-valued function.

See Logistic regression and Newton's method

Normal distribution

In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.

See Logistic regression and Normal distribution

Normalizing constant

In probability theory, a normalizing constant or normalizing factor is used to reduce any probability function to a probability density function with total probability of one.

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Null hypothesis

In scientific research, the null hypothesis (often denoted H0) is the claim that the effect being studied does not exist.

See Logistic regression and Null hypothesis

Null model

In mathematics, for example in the study of statistical properties of graphs, a null model is a type of random object that matches one specific object in some of its features, or more generally satisfies a collection of constraints, but which is otherwise taken to be an unbiasedly random structure.

See Logistic regression and Null model

Observational study

In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints.

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Odds

In probability theory, odds provide a measure of the probability of a particular outcome.

See Logistic regression and Odds

Odds ratio

An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. The odds ratio is defined as the ratio of the odds of event A taking place in the presence of B, the and odds of A in the absence of B. Due to symmetry, odds ratio reciprocally calculates the ratio of the odds of B occurring in the presence of A, and the odds of B in the absence of A.

See Logistic regression and Odds ratio

One in ten rule

In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis (in particular proportional hazards models in survival analysis and logistic regression) while keeping the risk of overfitting and finding spurious correlations low.

See Logistic regression and One in ten rule

OpenBUGS

OpenBUGS is a software application for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods.

See Logistic regression and OpenBUGS

Ordered logit

In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh.

See Logistic regression and Ordered logit

Ordinary least squares

In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable.

See Logistic regression and Ordinary least squares

Overfitting

In mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably".

See Logistic regression and Overfitting

Parti Québécois

The paren,; PQ) is a sovereignist and social democratic provincial political party in Quebec, Canada. The PQ advocates national sovereignty for Quebec involving independence of the province of Quebec from Canada and establishing a sovereign state. The PQ has also promoted the possibility of maintaining a loose political and economic sovereignty-association between Quebec and Canada.

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Partition of sums of squares

The partition of sums of squares is a concept that permeates much of inferential statistics and descriptive statistics.

See Logistic regression and Partition of sums of squares

Perceptron

In machine learning, the perceptron (or McCulloch–Pitts neuron) is an algorithm for supervised learning of binary classifiers.

See Logistic regression and Perceptron

Philosophical Transactions of the Royal Society A

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences is a fortnightly peer-reviewed scientific journal published by the Royal Society.

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Pierre François Verhulst

Pierre François Verhulst (28 October 1804, in Brussels – 15 February 1849, in Brussels) was a Belgian mathematician and a doctor in number theory from the University of Ghent in 1825.

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Poisson regression

In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables.

See Logistic regression and Poisson regression

Political science

Political science is the scientific study of politics.

See Logistic regression and Political science

Polynomial regression

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x).

See Logistic regression and Polynomial regression

Population growth

Population growth is the increase in the number of people in a population or dispersed group.

See Logistic regression and Population growth

Posterior probability

The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule.

See Logistic regression and Posterior probability

Prior probability

A prior probability distribution of an uncertain quantity, often simply called the prior, is its assumed probability distribution before some evidence is taken into account.

See Logistic regression and Prior probability

Probabilistic programming

Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically.

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Probability

Probability is the branch of mathematics concerning events and numerical descriptions of how likely they are to occur.

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Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of possible outcomes for an experiment.

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Probability mass function

In probability and statistics, a probability mass function (sometimes called probability function or frequency function) is a function that gives the probability that a discrete random variable is exactly equal to some value.

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Probit

In probability theory and statistics, the probit function is the quantile function associated with the standard normal distribution.

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Probit model

In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married.

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Proceedings of the National Academy of Sciences of the United States of America

Proceedings of the National Academy of Sciences of the United States of America (often abbreviated PNAS or PNAS USA) is a peer-reviewed multidisciplinary scientific journal.

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PyMC

PyMC (formerly known as PyMC3) is a probabilistic programming language written in Python.

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Quasi-Newton method

Quasi-Newton methods are methods used to find either zeroes or local maxima and minima of functions, as an alternative to Newton's method.

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Quebec

QuebecAccording to the Canadian government, Québec (with the acute accent) is the official name in Canadian French and Quebec (without the accent) is the province's official name in Canadian English is one of the thirteen provinces and territories of Canada.

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Random variable

A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events.

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Rational choice theory

Rational choice theory refers to a set of guidelines that help understand economic and social behaviour.

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Raymond Pearl

Raymond Pearl (June 3, 1879 – November 17, 1940) was an American biologist, regarded as one of the founders of biogerontology.

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Real number

In mathematics, a real number is a number that can be used to measure a continuous one-dimensional quantity such as a distance, duration or temperature.

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Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features').

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Regularization (mathematics)

In mathematics, statistics, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler".

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Ridge regression

Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated.

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Robust statistics

Robust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect.

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Ronald Fisher

Sir Ronald Aylmer Fisher (17 February 1890 – 29 July 1962) was a British polymath who was active as a mathematician, statistician, biologist, geneticist, and academic.

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Scalar (mathematics)

A scalar is an element of a field which is used to define a vector space.

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Scale parameter

In probability theory and statistics, a scale parameter is a special kind of numerical parameter of a parametric family of probability distributions.

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Science (journal)

Science, also widely referred to as Science Magazine, is the peer-reviewed academic journal of the American Association for the Advancement of Science (AAAS) and one of the world's top academic journals.

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Separation (statistics)

In statistics, separation is a phenomenon associated with models for dichotomous or categorical outcomes, including logistic and probit regression.

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Sigmoid function

A sigmoid function is any mathematical function whose graph has a characteristic S-shaped or sigmoid curve.

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Softmax function

The softmax function, also known as softargmax or normalized exponential function, converts a vector of real numbers into a probability distribution of possible outcomes.

See Logistic regression and Softmax function

Sparse matrix

In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero.

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Spline (mathematics)

In mathematics, a spline is a function defined piecewise by polynomials.

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Stan (software)

Stan is a probabilistic programming language for statistical inference written in C++.

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Statistical classification

When classification is performed by a computer, statistical methods are normally used to develop the algorithm.

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Statistical data type

In statistics, groups of individual data points may be classified as belonging to any of various statistical data types, e.g. categorical ("red", "blue", "green"), real number, odd number (1,3,5) etc.

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Statistical model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population).

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Statistics

Statistics (from German: Statistik, "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.

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Step function

In mathematics, a function on the real numbers is called a step function if it can be written as a finite linear combination of indicator functions of intervals.

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Stratification (clinical trials)

Stratification of clinical trials is the partitioning of subjects and results by a factor other than the treatment given.

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Supervised learning

Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model.

See Logistic regression and Supervised learning

Trauma Quality Improvement Program

The Trauma Quality Improvement Program (TQIP) was initiated in 2008 by the American College of Surgeons Committee on Trauma.

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Type I and type II errors

In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true.

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Udny Yule

George Udny Yule, CBE, FRS (18 February 1871 – 26 June 1951), usually known as Udny Yule, was a British statistician, particularly known for the Yule distribution and proposing the preferential attachment model for random graphs.

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Unit of measurement

A unit of measurement, or unit of measure, is a definite magnitude of a quantity, defined and adopted by convention or by law, that is used as a standard for measurement of the same kind of quantity.

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Utility

In economics, utility is a measure of the satisfaction that a certain person has from a certain state of the world.

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Value (mathematics)

In mathematics, value may refer to several, strongly related notions.

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Variational Bayesian methods

Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.

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Wald test

In statistics, the Wald test (named after Abraham Wald) assesses constraints on statistical parameters based on the weighted distance between the unrestricted estimate and its hypothesized value under the null hypothesis, where the weight is the precision of the estimate.

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Wilhelm Ostwald

Friedrich Wilhelm Ostwald (4 April 1932) was a Baltic German chemist and philosopher.

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Y-intercept

In analytic geometry, using the common convention that the horizontal axis represents a variable x and the vertical axis represents a variable y, a y-intercept or vertical intercept is a point where the graph of a function or relation intersects the y-axis of the coordinate system.

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References

[1] https://en.wikipedia.org/wiki/Logistic_regression

Also known as Applications of logistic regression, Binary logit model, Conditional logit analysis, Logit model, Logit regression.

, Edwin Bidwell Wilson, Emergency management, Engineering, Entropy (information theory), Error function, Estimation theory, Euler numbers, Expectation propagation, Expected value, Exponential family, Exponential function, Generalized linear model, Goodness of fit, Gradient descent, Heavy-tailed distribution, Hosmer–Lemeshow test, Identifiability, Independence of irrelevant alternatives, Independent and identically distributed random variables, Indicator function, Information content, Iteratively reweighted least squares, Jane Worcester, Jarrow–Turnbull model, John Gaddum, Joseph Berkson, Journal of Clinical Epidemiology, Just another Gibbs sampler, Kullback–Leibler divergence, L. Gustave du Pasquier, Lagrange multiplier, Latent and observable variables, Latent variable model, Likelihood function, Likelihood-ratio test, Limited dependent variable, Limited-memory BFGS, Linear combination, Linear discriminant analysis, Linear function (calculus), Linear least squares, Linear model, Linear predictor function, Linear regression, Local case-control sampling, Location parameter, Logarithm, Logistic distribution, Logistic function, Logistic model tree, Logit, Loss function, Loss functions for classification, Lowell Reed, Luce's choice axiom, Machine learning, Mark Thoma, Marketing, Matching (statistics), Maximum a posteriori estimation, Maximum entropy probability distribution, Maximum likelihood estimation, Mixed logit, Mlpack, Mortgage, Multicollinearity, Multinomial logistic regression, Myocardial infarction, Natural language processing, Natural logarithm, Neural network (machine learning), Newton's method, Normal distribution, Normalizing constant, Null hypothesis, Null model, Observational study, Odds, Odds ratio, One in ten rule, OpenBUGS, Ordered logit, Ordinary least squares, Overfitting, Parti Québécois, Partition of sums of squares, Perceptron, Philosophical Transactions of the Royal Society A, Pierre François Verhulst, Poisson regression, Political science, Polynomial regression, Population growth, Posterior probability, Prior probability, Probabilistic programming, Probability, Probability distribution, Probability mass function, Probit, Probit model, Proceedings of the National Academy of Sciences of the United States of America, PyMC, Quasi-Newton method, Quebec, Random variable, Rational choice theory, Raymond Pearl, Real number, Regression analysis, Regularization (mathematics), Ridge regression, Robust statistics, Ronald Fisher, Scalar (mathematics), Scale parameter, Science (journal), Separation (statistics), Sigmoid function, Softmax function, Sparse matrix, Spline (mathematics), Stan (software), Statistical classification, Statistical data type, Statistical model, Statistics, Step function, Stratification (clinical trials), Supervised learning, Trauma Quality Improvement Program, Type I and type II errors, Udny Yule, Unit of measurement, Utility, Value (mathematics), Variational Bayesian methods, Wald test, Wilhelm Ostwald, Y-intercept.

Logistic regression, the Glossary

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