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152 relations: Analytic function, Artificial neural network, Backpropagation, Bayesian statistics, Bernoulli distribution, Bernoulli trial, Binary data, Binomial distribution, Blood pressure, Blood test, Blood type, Body mass index, Brier score, C (programming language), C++, Cambridge University Press, Canada, Cardinal number, Categorical variable, Chi-squared distribution, Coefficient of determination, Computer science, Conditional entropy, Conditional logistic regression, Conditional random field, Conjugate prior, Contingency table, Continuous or discrete variable, Coronary artery disease, Cumulative distribution function, David A. Freedman, David Cox (statistician), Degrees of freedom (statistics), Democratic Party (United States), Dependent and independent variables, Design matrix, Deviance (statistics), Diabetes mellitus, Discrete choice, Dot product, Dummy variable (statistics), Econometrics, Economics, Engineering, Estimation theory, Expected value, Exponential function, Generalized extreme value distribution, Generalized linear model, Goodness of fit, ..., Gradient descent, Heavy-tailed distribution, Heteroscedasticity, Homoscedasticity, Hosmer–Lemeshow test, Identifiability, Independent and identically distributed random variables, Iteratively reweighted least squares, Jarrow–Turnbull model, Journal of Clinical Epidemiology, Just another Gibbs sampler, Kullback–Leibler divergence, Latent variable, 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 (mathematics), Linear predictor function, Linear regression, List of statistical packages, Local case-control sampling, Logarithm, Logistic distribution, Logistic function, Logistic model tree, Logit, Machine learning, Mark Thoma, Marketing, Matching (statistics), MATLAB, Maximum a posteriori estimation, Maximum likelihood estimation, Microsoft Excel, Mixed logit, MLPACK (C++ library), Mortgage loan, Multicollinearity, Multilevel model, Multinomial logistic regression, Myocardial infarction, Natural language processing, Natural logarithm, NCSS (statistical software), Newton's method, Normal distribution, Normalizing constant, Observational study, Odds, One in ten rule, OpenBUGS, Ordered logit, Parti Québécois, Partition of sums of squares, Perceptron, Political science, Polynomial regression, Posterior probability, Prior probability, Probability, Probability distribution, Probability mass function, Probit model, Python (programming language), Quasi-Newton method, Quebec, R (programming language), Random variable, Rational choice theory, Real number, Regression analysis, Regularization (mathematics), Republican Party (United States), Robust statistics, SAS (software), Scikit-learn, Separation (statistics), Sigmoid function, Softmax function, Sparse matrix, Spline (mathematics), Stan (software), Statistical classification, Statistical data type, Statistical model, Statistics, Statsmodels, Step function, Stratification (clinical trials), TensorFlow, Tikhonov regularization, Type I and type II errors, Utility, Value (mathematics), Variance, Wald test, Y-intercept. Expand index (102 more) »
Analytic function
In mathematics, an analytic function is a function that is locally given by a convergent power series.
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Artificial neural network
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
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Backpropagation
Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network.
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Bayesian statistics
Bayesian statistics, named for Thomas Bayes (1701–1761), is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief known as Bayesian probabilities.
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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 data
Binary data is data whose unit can take on only two possible states, traditionally termed 0 and +1 in accordance with the binary numeral system and Boolean algebra.
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Binomial distribution
In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability p) or failure/no/false/zero (with probability q.
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Blood pressure
Blood pressure (BP) is the pressure of circulating blood on the walls of blood vessels.
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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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Blood type
A blood type (also called a blood group) is a classification of blood based on the presence and absence of antibodies and also based on the presence or absence of inherited antigenic substances on the surface of red blood cells (RBCs).
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Body mass index
The body mass index (BMI) or Quetelet index is a value derived from the mass (weight) and height of an individual.
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Brier score
The Brier score is a proper score function that measures the accuracy of probabilistic predictions.
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C (programming language)
C (as in the letter ''c'') is a general-purpose, imperative computer programming language, supporting structured programming, lexical variable scope and recursion, while a static type system prevents many unintended operations.
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C++
C++ ("see plus plus") is a general-purpose programming language.
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Cambridge University Press
Cambridge University Press (CUP) is the publishing business of the University of Cambridge.
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Canada
Canada is a country located in the northern part of North America.
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Cardinal number
In mathematics, cardinal numbers, or cardinals for short, are a generalization of the natural numbers used to measure the cardinality (size) of sets.
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Categorical variable
In statistics, a categorical 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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Chi-squared distribution
No description.
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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 variance in the dependent variable that is predictable from the independent variable(s).
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Computer science
Computer science deals with the theoretical foundations of information and computation, together with practical techniques for the implementation and application of these foundations.
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Conditional entropy
In information theory, the conditional entropy (or equivocation) 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 take into account stratification and matching.
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Conditional random field
Conditional random fields (CRFs) are a class of statistical modeling method often applied in pattern recognition and machine learning and used for structured prediction.
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Conjugate prior
In Bayesian probability theory, if the posterior distributions p(θ|x) are in the same probability distribution family as the prior probability distribution p(θ), the prior and posterior are then called conjugate distributions, and the prior is called a conjugate prior for the likelihood function.
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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, a variable may be continuous or discrete.
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Coronary artery disease
Coronary artery disease (CAD), also known as ischemic heart disease (IHD), refers to a group of diseases which includes stable angina, unstable angina, myocardial infarction, and sudden cardiac death.
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Cumulative distribution function
In probability theory and statistics, the cumulative distribution function (CDF, also cumulative density function) 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. In the case of a continuous distribution, it gives the area under the probability density function from minus infinity to x. Cumulative distribution functions are also used to specify the distribution of multivariate random variables.
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David A. Freedman
David Amiel Freedman (5 March 1938 – 17 October 2008) was Professor of Statistics at the University of California, Berkeley.
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David Cox (statistician)
Sir David Roxbee Cox (born 15 July 1924) is a prominent British statistician.
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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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Democratic Party (United States)
The Democratic Party is one of the two major contemporary political parties in the United States, along with the Republican Party (nicknamed the GOP for Grand Old Party).
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Dependent and independent variables
In mathematical modeling, statistical modeling and experimental sciences, the values of dependent variables depend on the values of independent variables.
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Design matrix
In statistics, a design matrix, also known as model matrix or regressor matrix, is a matrix of values of explanatory variables of a set of objects, often denoted by X. Each row represents an individual object, with the successive columns corresponding to the variables and their specific values for that object.
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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 mellitus
Diabetes mellitus (DM), commonly referred to as diabetes, is a group of metabolic disorders in which there are high blood sugar levels over a prolonged period.
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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 is often also used more generally to mean a symmetric bilinear form, for example for a pseudo-Euclidean space.
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Dummy variable (statistics)
In statistics and econometrics, particularly in regression analysis, a dummy variable (also known as an indicator variable, design variable, Boolean indicator, binary variable, or qualitative variable) is one that takes the 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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Econometrics
Econometrics is the application of statistical methods to economic data and is described as the branch of economics that aims to give empirical content to economic relations.
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Economics
Economics is the social science that studies the production, distribution, and consumption of goods and services.
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Engineering
Engineering is the creative application of science, mathematical methods, and empirical evidence to the innovation, design, construction, operation and maintenance of structures, machines, materials, devices, systems, processes, and organizations.
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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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Expected value
In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.
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Exponential function
In mathematics, an exponential function is a function of the form in which the argument occurs as an exponent.
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Generalized extreme value distribution
In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions.
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Generalized linear model
In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.
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Goodness of fit
The goodness of fit of a statistical model describes how well it fits a set of observations.
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Gradient descent
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
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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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Heteroscedasticity
In statistics, a collection of random variables is heteroscedastic (or heteroskedastic; from Ancient Greek hetero “different” and skedasis “dispersion”) if there are sub-populations that have different variabilities from others.
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Homoscedasticity
In statistics, a sequence or a vector of random variables is homoscedastic if all random variables in the sequence or vector have the same finite variance.
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Hosmer–Lemeshow test
The Hosmer–Lemeshow test is a statistical test for goodness of fit for logistic regression models.
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Identifiability
In statistics, identifiability is a property which a model must satisfy in order for precise inference to be possible.
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Independent and identically distributed random variables
In probability theory and statistics, a sequence or other collection of random variables is independent and identically distributed (i.i.d. or iid or IID) if each random variable has the same probability distribution as the others and all are mutually independent.
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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: by an iterative method in which each step involves solving a weighted least squares problem of the form:C.
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Jarrow–Turnbull model
The Jarrow–Turnbull credit risk model was published by Robert A. Jarrow of Kamakura Corporation and Cornell University and Stuart Turnbull, currently at the University of Houston.
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Journal of Clinical Epidemiology
The Journal of Clinical Epidemiology is a peer reviewed journal of Epidemiology that promotes the quality of clinical and patient-oriented health services research through the advancement and application of innovative methods of.
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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 divergence (also called relative entropy) is a measure of how one probability distribution diverges from a second, expected probability distribution.
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Latent variable
In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured).
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Latent variable model
A latent variable model is a statistical model that relates a set of observable variables (so-called manifest variables) to a set of latent variables.
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Likelihood function
In frequentist inference, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model, given specific observed data.
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Likelihood-ratio test
In statistics, a likelihood ratio test (LR test) is a statistical test used for comparing the goodness of fit of two statistical models — a null model against an alternative model.
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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.
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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 (BFGS) algorithm using a limited amount of computer memory.
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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).
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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, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.
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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 with uniform scales) is a line in the plane.
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Linear least squares (mathematics)
In statistics and mathematics, linear least squares is an approach to fitting a mathematical or statistical model to data in cases where the idealized value provided by the model for any data point is expressed linearly in terms of the unknown parameters of the model.
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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.
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Linear regression
In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).
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List of statistical packages
Statistical software are specialized computer programs for analysis in statistics and econometrics.
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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.
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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.
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Logistic function
A logistic function or logistic curve is a common "S" shape (sigmoid curve), with equation: where.
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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.
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Logit
The logit function is the inverse of the sigmoidal "logistic" function or logistic transform used in mathematics, especially in statistics.
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Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
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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 study and management of exchange relationships.
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Matching (statistics)
Matching is a statistical technique which is used to evaluate 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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MATLAB
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and proprietary programming language developed by MathWorks.
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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 likelihood estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model, given observations.
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Microsoft Excel
Microsoft Excel is a spreadsheet developed by Microsoft for Windows, macOS, Android and iOS.
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Mixed logit
Mixed logit is a fully general statistical model for examining discrete choices.
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MLPACK (C++ library)
mlpack is a machine learning software library for C++, built on top of the Armadillo library.
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Mortgage loan
A mortgage loan, or simply mortgage, is used either by purchasers of real property to raise funds to buy real estate, or alternatively by existing property owners to raise funds for any purpose, while putting a lien on the property being mortgaged.
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Multicollinearity
In statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.
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Multilevel model
Multilevel models (also known as hierarchical linear models, nested data models, mixed models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level.
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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.
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Myocardial infarction
Myocardial infarction (MI), commonly known as a heart attack, occurs when blood flow decreases or stops to a part of the heart, causing damage to the heart muscle.
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Natural language processing
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
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Natural logarithm
The natural logarithm of a number is its logarithm to the base of the mathematical constant ''e'', where e is an irrational and transcendental number approximately equal to.
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NCSS (statistical software)
NCSS is a statistics package produced and distributed by NCSS, LLC.
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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 method for finding successively better approximations to the roots (or zeroes) of a real-valued function.
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Normal distribution
In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.
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Normalizing constant
The concept of a normalizing constant arises in probability theory and a variety of other areas of mathematics.
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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
Odds are a numerical expression, usually expressed as a pair of numbers, used in both gambling and statistics.
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One in ten rule
In statistics, the one in ten rule is a rule of thumb for how many predictors can be derived from data when doing regression analysis (in particular proportional hazards models and logistic regression) without risk of overfitting.
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OpenBUGS
OpenBUGS is a software application for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods.
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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.
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Parti Québécois
The Parti Québécois (French for Quebec Party; PQ) is a sovereignist provincial political party in Quebec in 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.
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Perceptron
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).
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Political science
Political science is a social science which deals with systems of governance, and the analysis of political activities, political thoughts, and political behavior.
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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 modelled 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), and has been used to describe nonlinear phenomena such as the growth rate of tissues, the distribution of carbon isotopes in lake sediments, and the progression of disease epidemics.
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Posterior probability
In Bayesian statistics, the posterior probability of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account.
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Prior probability
In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account.
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Probability
Probability is the measure of the likelihood that an event will occur.
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Probability distribution
In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.
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Probability mass function
In probability and statistics, a probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.
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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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Python (programming language)
Python is an interpreted high-level programming language for general-purpose programming.
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Quasi-Newton method
Quasi-Newton methods are methods used to either find zeroes or local maxima and minima of functions, as an alternative to Newton's method.
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Quebec
Quebec (Québec)According to the Canadian government, Québec (with the acute accent) is the official name in French and Quebec (without the accent) is the province's official name in English; the name is.
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R (programming language)
R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.
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Random variable
In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.
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Rational choice theory
Rational choice theory, also known as choice theory or rational action theory, is a framework for understanding and often formally modeling social and economic behavior.
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Real number
In mathematics, a real number is a value of a continuous quantity that can represent a distance along a line.
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Regression analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables.
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Regularization (mathematics)
In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.
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Republican Party (United States)
The Republican Party, also referred to as the GOP (abbreviation for Grand Old Party), is one of the two major political parties in the United States, the other being its historic rival, the Democratic Party.
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Robust statistics
Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal.
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SAS (software)
SAS (previously "Statistical Analysis System") is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.
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Scikit-learn
Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.
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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 a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.
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Softmax function
In mathematics, the softmax function, or normalized exponential function, is a generalization of the logistic function that "squashes" a -dimensional vector \mathbf of arbitrary real values to a -dimensional vector \sigma(\mathbf) of real values, where each entry is in the range (0, 1, and all the entries add up to 1. The function is given by In probability theory, the output of the softmax function can be used to represent a categorical distribution – that is, a probability distribution over different possible outcomes. In fact, it is the gradient-log-normalizer of the categorical probability distribution. The softmax function is also the gradient of the LogSumExp function. The softmax function is used in various multiclass classification methods, such as multinomial logistic regression (also known as softmax regression), multiclass linear discriminant analysis, naive Bayes classifiers, and artificial neural networks. Specifically, in multinomial logistic regression and linear discriminant analysis, the input to the function is the result of distinct linear functions, and the predicted probability for the 'th class given a sample vector and a weighting vector is: This can be seen as the composition of linear functions \mathbf \mapsto \mathbf^\mathsf\mathbf_1, \ldots, \mathbf \mapsto \mathbf^\mathsf\mathbf_K and the softmax function (where \mathbf^\mathsf\mathbf denotes the inner product of \mathbf and \mathbf). The operation is equivalent to applying a linear operator defined by \mathbf to vectors \mathbf, thus transforming the original, probably highly-dimensional, input to vectors in a -dimensional space \mathbb^K.
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Sparse matrix
In numerical analysis and computer science, 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
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
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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 (1.68, -5, 1.7e+6),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 some sample data and similar data from a larger population.
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Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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Statsmodels
Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests.
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Step function
In mathematics, a function on the real numbers is called a step function (or staircase 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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TensorFlow
TensorFlow is an open-source software library for dataflow programming across a range of tasks.
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Tikhonov regularization
Tikhonov regularization, named for Andrey Tikhonov, is the most commonly used method of regularization of ill-posed problems.
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Type I and type II errors
In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is failing to reject a false null hypothesis (also known as a "false negative" finding).
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Utility
Within economics the concept of utility is used to model worth or value, but its usage has evolved significantly over time.
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Value (mathematics)
In mathematics, value may refer to several, strongly related notions.
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Variance
In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.
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Wald test
The Wald test is a parametric statistical test named after the statistician Abraham Wald.
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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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Binary logit model, Conditional logit analysis, Logistic Regression, Logit model, Logit regression.
References
[1] https://en.wikipedia.org/wiki/Logistic_regression
