In
statisticsStatistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....
,
regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more
independent variableThe terms "dependent variable" and "independent variable" are used in similar but subtly different ways in mathematics and statistics as part of the standard terminology in those subjects...
s. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the
conditional expectationIn probability theory, a conditional expectation is the expected value of a real random variable with respect to a conditional probability distribution....
of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed. Less commonly, the focus is on a
quantileQuantiles are points taken at regular intervals from the cumulative distribution function of a random variable. Dividing ordered data into q essentially equalsized data subsets is the motivation for qquantiles; the quantiles are the data values marking the boundaries between consecutive subsets...
, or other
location parameterIn statistics, a location family is a class of probability distributions that is parametrized by a scalar or vectorvalued parameter μ, which determines the "location" or shift of the distribution...
of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a
functionIn mathematics, a function associates one quantity, the argument of the function, also known as the input, with another quantity, the value of the function, also known as the output. A function assigns exactly one output to each input. The argument and the value may be real numbers, but they can...
of the independent variables called the
regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a
probability distributionIn probability theory, a probability mass, probability density, or probability distribution is a function that describes the probability of a random variable taking certain values....
.
Regression analysis is widely used for
predictionA prediction or forecast is a statement about the way things will happen in the future, often but not always based on experience or knowledge...
and
forecastForecasting is the process of making statements about events whose actual outcomes have not yet been observed. A commonplace example might be estimation for some variable of interest at some specified future date. Prediction is a similar, but more general term...
ing, where its use has substantial overlap with the field of
machine learningMachine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer
causal relationshipsCausality is the relationship between an event and a second event , where the second event is understood as a consequence of the first....
between the independent and dependent variables.
A large body of techniques for carrying out regression analysis has been developed. Familiar methods such as
linear regressionIn statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple regression...
and
ordinary least squaresIn statistics, ordinary least squares or linear least squares is a method for estimating the unknown parameters in a linear regression model. This method minimizes the sum of squared vertical distances between the observed responses in the dataset and the responses predicted by the linear...
regression are
parametricParametric statistics is a branch of statistics that assumes that the data has come from a type of probability distribution and makes inferences about the parameters of the distribution. Most wellknown elementary statistical methods are parametric....
, in that the regression function is defined in terms of a finite number of unknown
parameterParameter from Ancient Greek παρά also “para” meaning “beside, subsidiary” and μέτρον also “metron” meaning “measure”, can be interpreted in mathematics, logic, linguistics, environmental science and other disciplines....
s that are estimated from the
dataThe term data refers to qualitative or quantitative attributes of a variable or set of variables. Data are typically the results of measurements and can be the basis of graphs, images, or observations of a set of variables. Data are often viewed as the lowest level of abstraction from which...
.
Nonparametric regressionNonparametric regression is a form of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data...
refers to techniques that allow the regression function to lie in a specified set of
functionsIn mathematics, a function associates one quantity, the argument of the function, also known as the input, with another quantity, the value of the function, also known as the output. A function assigns exactly one output to each input. The argument and the value may be real numbers, but they can...
, which may be
infinitedimensionalIn physics and mathematics, the dimension of a space or object is informally defined as the minimum number of coordinates needed to specify any point within it. Thus a line has a dimension of one because only one coordinate is needed to specify a point on it...
.
The performance of regression analysis methods in practice depends on the form of the
data generating processThe term data generating process is used in statistical and scientific literature to convey a number of different ideas:*the data collection process, being routes and procedures by which data reach a database ;...
, and how it relates to the regression approach being used. Since the true form of the datagenerating process is in general not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes (but not always) testable if a large amount of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However, in many applications, especially with small
effectsIn statistics, an effect size is a measure of the strength of the relationship between two variables in a statistical population, or a samplebased estimate of that quantity...
or questions of
causalityCausality is the relationship between an event and a second event , where the second event is understood as a consequence of the first....
based on
observational dataIn epidemiology and statistics, an observational study draws inferences about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator...
, regression methods give misleading results.
History
The earliest form of regression was the method of least squares (French:
méthode des moindres carrés), which was published by Legendre in 1805, and by
GaussJohann Carl Friedrich Gauss was a German mathematician and scientist who contributed significantly to many fields, including number theory, statistics, analysis, differential geometry, geodesy, geophysics, electrostatics, astronomy and optics.Sometimes referred to as the Princeps mathematicorum...
in 1809. Legendre and Gauss both applied the method to the problem of determining, from astronomical observations, the orbits of bodies about the Sun. Gauss published a further development of the theory of least squares in 1821, including a version of the
Gauss–Markov theoremIn statistics, the Gauss–Markov theorem, named after Carl Friedrich Gauss and Andrey Markov, states that in a linear regression model in which the errors have expectation zero and are uncorrelated and have equal variances, the best linear unbiased estimator of the coefficients is given by the...
.
The term "regression" was coined by
Francis GaltonSir Francis Galton /ˈfrɑːnsɪs ˈgɔːltn̩/ FRS , cousin of Douglas Strutt Galton, halfcousin of Charles Darwin, was an English Victorian polymath: anthropologist, eugenicist, tropical explorer, geographer, inventor, meteorologist, protogeneticist, psychometrician, and statistician...
in the nineteenth century to describe a biological phenomenon. The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as
regression toward the meanIn statistics, regression toward the mean is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on a second measurement, and—a fact that may superficially seem paradoxical—if it is extreme on a second measurement, will tend...
).
For Galton, regression had only this biological meaning, but his work was later extended by
Udny YuleGeorge Udny Yule FRS , usually known as Udny Yule, was a British statistician, born at Beech Hill, a house in Morham near Haddington, Scotland and died in Cambridge, England. His father, also George Udny Yule, and a nephew, were knighted. His uncle was the noted orientalist Sir Henry Yule...
and
Karl PearsonKarl Pearson FRS was an influential English mathematician who has been credited for establishing the disciplineof mathematical statistics....
to a more general statistical context. In the work of Yule and Pearson, the joint distribution of the response and explanatory variables is assumed to be
Gaussian. This assumption was weakened by R.A. Fisher in his works of 1922 and 1925. Fisher assumed that the conditional distribution of the response variable is Gaussian, but the joint distribution need not be. In this respect, Fisher's assumption is closer to Gauss's formulation of 1821.
Regression methods continue to be an area of active research. In recent decades, new methods have been developed for
robust regressionIn robust statistics, robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and nonparametric methods. Regression analysis seeks to find the effect of one or more independent variables upon a dependent variable...
, regression involving correlated responses such as
time seriesIn statistics, signal processing, econometrics and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones index or the annual flow volume of the...
and growth curves, regression in which the predictor or response variables are curves, images, graphs, or other complex data objects, regression methods accommodating various types of missing data,
nonparametric regressionNonparametric regression is a form of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data...
,
BayesianBayesian statistics is that subset of the entire field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief or, more specifically, Bayesian probabilities...
methods for regression, regression in which the predictor variables are measured with error, regression with more predictor variables than observations, and causal inference with regression.
Regression models
Regression models involve the following variables:
 The unknown parameters denoted as β; this may be a scalar or a vector.
 The independent variables, X.
 The dependent variable, Y.
In various
fields of application, different terminologies are used in place of
dependent and independent variablesThe terms "dependent variable" and "independent variable" are used in similar but subtly different ways in mathematics and statistics as part of the standard terminology in those subjects...
.
A regression model relates
Y to a function of
X and
β.
The approximation is usually formalized as
E(
Y 
X) =
f(
X,
β). To carry out regression analysis, the form of the function
f must be specified. Sometimes the form of this function is based on knowledge about the relationship between
Y and
X that does not rely on the data. If no such knowledge is available, a flexible or convenient form for
f is chosen.
Assume now that the vector of unknown parameters
β is of length
k. In order to perform a regression analysis the user must provide information about the dependent variable
Y:
 If N data points of the form (Y,X) are observed, where N < k, most classical approaches to regression analysis cannot be performed: since the system of equations defining the regression model is underdetermined, there is not enough data to recover β.
 If exactly N = k data points are observed, and the function f is linear, the equations Y = f(X, β) can be solved exactly rather than approximately. This reduces to solving a set of N equations with N unknowns (the elements of β), which has a unique solution as long as the X are linearly independent. If f is nonlinear, a solution may not exist, or many solutions may exist.
 The most common situation is where N > k data points are observed. In this case, there is enough information in the data to estimate a unique value for β that best fits the data in some sense, and the regression model when applied to the data can be viewed as an overdetermined system
In mathematics, a system of linear equations is considered overdetermined if there are more equations than unknowns. The terminology can be described in terms of the concept of counting constraints. Each unknown can be seen as an available degree of freedom...
in β.
In the last case, the regression analysis provides the tools for:
 Finding a solution for unknown parameters β that will, for example, minimize the distance between the measured and predicted values of the dependent variable Y (also known as method of least squares
The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. "Least squares" means that the overall solution minimizes the sum of the squares of the errors made in solving every...
).
 Under certain statistical assumptions, the regression analysis uses the surplus of information to provide statistical information about the unknown parameters β and predicted values of the dependent variable Y.
Necessary number of independent measurements
Consider a regression model which has three unknown parameters, β
_{0}, β
_{1}, and β
_{2}. Suppose an experimenter performs 10 measurements all at exactly the same value of independent variable vector
X (which contains the independent variables
X_{1},
X_{2}, and
X_{3}). In this case, regression analysis fails to give a unique set of estimated values for the three unknown parameters; the experimenter did not provide enough information. The best one can do is to estimate the average value and the standard deviation of the dependent variable
Y. Similarly, measuring at two different values of
X would give enough data for a regression with two unknowns, but not for three or more unknowns.
If the experimenter had performed measurements at three different values of the independent variable vector
X, then regression analysis would provide a unique set of estimates for the three unknown parameters in
β.
In the case of general linear regression, the above statement is equivalent to the requirement that matrix
X^{T}X is
invertible.
Statistical assumptions
When the number of measurements,
N, is larger than the number of unknown parameters,
k, and the measurement errors ε
_{i} are normally distributed then
the excess of information contained in (
N 
k) measurements is used to make statistical predictions about the unknown parameters. This excess of information is referred to as the degrees of freedom of the regression.
Underlying assumptions
Classical assumptions for regression analysis include:
 The sample is representative of the population for the inference prediction.
 The error is a random variable
In probability and statistics, a random variable or stochastic variable is, roughly speaking, a variable whose value results from a measurement on some type of random process. Formally, it is a function from a probability space, typically to the real numbers, which is measurable functionmeasurable...
with a mean of zero conditional on the explanatory variables.
 The independent variables are measured with no error. (Note: If this is not so, modeling may be done instead using errorsinvariables model
Total least squares, also known as errors in variables, rigorous least squares, or orthogonal regression, is a least squares data modeling technique in which observational errors on both dependent and independent variables are taken into account...
techniques).
 The predictors are linearly independent, i.e. it is not possible to express any predictor as a linear combination of the others. See Multicollinearity
Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In this situation the coefficient estimates may change erratically in response to small changes in the model or the data...
.
 The errors are uncorrelated
In probability theory and statistics, two realvalued random variables are said to be uncorrelated if their covariance is zero. Uncorrelatedness is by definition pairwise; i.e...
, that is, the variancecovariance matrix of the errors is diagonalIn linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero. The diagonal entries themselves may or may not be zero...
and each nonzero element is the variance of the error.
 The variance of the error is constant across observations (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. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity...
). (Note: If not, weighted least squares or other methods might instead be used).
These are sufficient conditions for the leastsquares estimator to possess desirable properties, in particular, these assumptions imply that the parameter estimates will be
unbiasedIn statistics, bias of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. Otherwise the estimator is said to be biased.In ordinary English, the term bias is...
,
consistentIn statistics, a sequence of estimators for parameter θ0 is said to be consistent if this sequence converges in probability to θ0...
, and efficient in the class of linear unbiased estimators. It is important to note that actual data rarely satisfies the assumptions. That is, the method is used even though the assumptions are not true. Variation from the assumptions can sometimes be used as a measure of how far the model is from being useful. Many of these assumptions may be relaxed in more advanced treatments. Reports of statistical analyses usually include analyses of tests on the sample data and methodology for the fit and usefulness of the model.
Assumptions include the geometrical support of the variables (Cressie, 1996). Independent and dependent variables often refer to values measured at point locations. There may be spatial trends and spatial autocorrelation in the variables that violates statistical assumptions of regression. Geographic weighted regression is one technique to deal with such data (Fotheringham et al., 2002). Also, variables may include values aggregated by areas. With aggregated data the
Modifiable Areal Unit ProblemThe modifiable areal unit problem is a source of statistical bias that can radically affect the results of statistical hypothesis tests. It affects results when pointbased measures of spatial phenomena are aggregated into districts. The resulting summary values are influenced by the choice of...
can cause extreme variation in regression parameters (Fotheringham and Wong, 1991). When analyzing data aggregated by political boundaries, postal codes or census areas results may be very different with a different choice of units.
Linear regression
In linear regression, the model specification is that the dependent variable,
is a
linear combinationIn mathematics, a linear combination is an expression constructed from a set of terms by multiplying each term by a constant and adding the results...
of the
parameters (but need not be linear in the
independent variables). For example, in
simple linear regressionIn statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model as...
for modeling
data points there is one independent variable:
, and two parameters,
and
:
 straight line:
In multiple linear regression, there are several independent variables or functions of independent variables. For example, adding a term in
x_{i}^{2} to the preceding regression gives:
 parabola:
This is still linear regression; although the expression on the right hand side is quadratic in the independent variable
, it is linear in the parameters
,
and
In both cases,
is an error term and the subscript
indexes a particular observation. Given a random sample from the population, we estimate the population parameters and obtain the sample linear regression model:

The
residualIn statistics and optimization, statistical errors and residuals are two closely related and easily confused measures of the deviation of a sample from its "theoretical value"...
,
, is the difference between the value of the dependent variable predicted by the model,
and the true value of the dependent variable
. One method of estimation is
ordinary least squaresIn statistics, ordinary least squares or linear least squares is a method for estimating the unknown parameters in a linear regression model. This method minimizes the sum of squared vertical distances between the observed responses in the dataset and the responses predicted by the linear...
. This method obtains parameter estimates that minimize the sum of squared
residualsIn statistics and optimization, statistical errors and residuals are two closely related and easily confused measures of the deviation of a sample from its "theoretical value"...
, SSE, also sometimes denoted
RSSIn statistics, the residual sum of squares is the sum of squares of residuals. It is also known as the sum of squared residuals or the sum of squared errors of prediction . It is a measure of the discrepancy between the data and an estimation model...
:
Minimization of this function results in a set of normal equations, a set of simultaneous linear equations in the parameters, which are solved to yield the parameter estimators,
.
In the case of simple regression, the formulas for the least squares estimates are
where
is the
meanIn mathematics and statistics, the arithmetic mean, often referred to as simply the mean or average when the context is clear, is a method to derive the central tendency of a sample space...
(average) of the
values and
is the mean of the
values. See
simple linear regressionIn statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model as...
for a derivation of these formulas and a numerical example.
Under the assumption that the population error term has a constant variance, the estimate of that variance is given by:

This is called the mean square error (MSE) of the regression.
The
standard errorThe standard error is the standard deviation of the sampling distribution of a statistic. The term may also be used to refer to an estimate of that standard deviation, derived from a particular sample used to compute the estimate....
s of the parameter estimates are given by
Under the further assumption that the population error term is normally distributed, the researcher can use these estimated standard errors to create
confidence intervalIn statistics, a confidence interval is a particular kind of interval estimate of a population parameter and is used to indicate the reliability of an estimate. It is an observed interval , in principle different from sample to sample, that frequently includes the parameter of interest, if the...
s and conduct hypothesis tests about the population parameters.
General linear model
In the more general multiple regression model, there are
p independent variables:

where
x_{ij} is the
i^{th} observation on the
j^{th} independent variable, and where the first independent variable takes the value 1 for all
i (so
is the regression intercept).
The least squares parameter estimates are obtained from
p normal equations. The residual can be written as
The
normal equations are
In matrix notation, the normal equations are written as
where the
ij element of
X is
x_{ij}, the
i element of the column vector
Y is
y_{i}, and the
j element of
is
. Thus
X is
n×
p,
Y is
n×1, and
is
p×1. The solution is
For a derivation, see linear least squares, and for a numerical example, see linear regression (example).
Regression diagnostics
Once a regression model has been constructed, it may be important to confirm the
goodness of fitThe goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g...
of the model and the
statistical significanceIn statistics, a result is called statistically significant if it is unlikely to have occurred by chance. The phrase test of significance was coined by Ronald Fisher....
of the estimated parameters. Commonly used checks of goodness of fit include the Rsquared, analyses of the pattern of
residualsIn statistics and optimization, statistical errors and residuals are two closely related and easily confused measures of the deviation of a sample from its "theoretical value"...
and hypothesis testing. Statistical significance can be checked by an
FtestAn Ftest is any statistical test in which the test statistic has an Fdistribution under the null hypothesis.It is most often used when comparing statistical models that have been fit to a data set, in order to identify the model that best fits the population from which the data were sampled. ...
of the overall fit, followed by ttests of individual parameters.
Interpretations of these diagnostic tests rest heavily on the model assumptions. Although examination of the residuals can be used to invalidate a model, the results of a ttest or
FtestAn Ftest is any statistical test in which the test statistic has an Fdistribution under the null hypothesis.It is most often used when comparing statistical models that have been fit to a data set, in order to identify the model that best fits the population from which the data were sampled. ...
are sometimes more difficult to interpret if the model's assumptions are violated. For example, if the error term does not have a normal distribution, in small samples the estimated parameters will not follow normal distributions and complicate inference. With relatively large samples, however, a
central limit theoremIn probability theory, the central limit theorem states conditions under which the mean of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed. The central limit theorem has a number of variants. In its common...
can be invoked such that hypothesis testing may proceed using asymptotic approximations.
Regression with "limited dependent" variables
The phrase "limited dependent" is used in econometric statistics for categorical and constrained variables.
The response variable may be noncontinuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with leastsquares linear regression, the model is called the linear probability model. Nonlinear models for binary dependent variables include the
probitIn statistics, a probit model is a type of regression where the dependent variable can only take two values, for example married or not married....
and
logit modelIn statistics, logistic regression is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve. It is a generalized linear model used for binomial regression...
. The
multivariate probitIn statistics and econometrics, the multivariate probit model is a generalization of the probit model used to estimate several correlated binary outcomes jointly...
model is a standard method of estimating a joint relationship between several binary dependent variables and some independent variables. For categorical variables with more than two values there is the
multinomial logitIn statistics, economics, and genetics, a multinomial logit model, also known as multinomial logistic regression, is a regression model which generalizes logistic regression by allowing more than two discrete outcomes...
. For ordinal variables with more than two values, there are the
ordered logitIn statistics, the ordered logit model , is a regression model for ordinal dependent variables...
and
ordered probitIn statistics, ordered probit is a generalization of the popular probit analysis to the case of more than two outcomes of an ordinal dependent variable. Similarly, the popular logit method also has a counterpart ordered logit....
models.
Censored regression modelCensored regression models commonly arise in econometrics in cases where the variable ofinterest is only observable under certain conditions. A common example is labor supply. Data are frequently available on the hours worked by employees, and a labor supply model estimates the relationship between...
s may be used when the dependent variable is only sometimes observed, and
Heckman correctionThe Heckman correction is any of a number of related statistical methods developed by James Heckman in 1976 through 1979 which allow the researcher to correct for selection bias...
type models may be used when the sample is not randomly selected from the population of interest. An alternative to such procedures is linear regression based on
polychoric correlationIn statistics, polychoric correlation is a technique for estimating the correlation between two theorised normally distributed continuous latent variables, from two observed ordinal variables. Tetrachoric correlation is a special case of the polychoric correlation applicable when both observed...
(or polyserial correlations) between the categorical variables. Such procedures differ in the assumptions made about the distribution of the variables in the population. If the variable is positive with low values and represents the repetition of the occurrence of an event, then count models like the
Poisson regressionIn statistics, Poisson regression is a form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown...
or the negative binomial model may be used.
Interpolation and extrapolation
Regression models predict a value of the
Y variable given known values of the
X variables. Prediction
within the range of values in the dataset used for modelfitting is known informally as
interpolationIn the mathematical field of numerical analysis, interpolation is a method of constructing new data points within the range of a discrete set of known data points....
. Prediction
outside this range of the data is known as
extrapolationIn mathematics, extrapolation is the process of constructing new data points. It is similar to the process of interpolation, which constructs new points between known points, but the results of extrapolations are often less meaningful, and are subject to greater uncertainty. It may also mean...
. Performing extrapolation relies strongly on the regression assumptions. The further the extrapolation goes outside the data, the more room there is for the model to fail due to differences between the assumptions and the sample data or the true values. For example, regression model failing the homoskedasticity assumption will make bigger mistakes the further out the extrapolation is .
It is generally advised that when performing extrapolation, one should accompany the estimated value of the dependent variable with a prediction interval that represents the uncertainty. Such intervals tend to expand rapidly as the values of the independent variable(s) moved outside the range covered by the observed data.
For such reasons and others, some tend to say that it might be unwise to undertake extrapolation.
However, this does not cover the full set of modelling errors that may be being made: in particular, the assumption of a particular form for the relation between
Y and
X. A properly conducted regression analysis will include an assessment of how well the assumed form is matched by the observed data, but it can only do so within the range of values of the independent variables actually available. This means that any extrapolation is particularly reliant on the assumptions being made about the structural form of the regression relationship. Bestpractice advice here is that a linearinvariables and linearinparameters relationship should not be chosen simply for computational convenience, but that all available knowledge should be deployed in constructing a regression model. If this knowledge includes the fact that the dependent variable cannot go outside a certain range of values, this can be made use of in selecting the model — even if the observed dataset has no values particularly near such bounds. The implications of this step of choosing an appropriate functional form for the regression can be great when extrapolation is considered. At a minimum, it can ensure that any extrapolation arising from a fitted model is "realistic" (or in accord with what is known).
Nonlinear regression
When the model function is not linear in the parameters, the sum of squares must be minimized by an iterative procedure. This introduces many complications which are summarized in Differences between linear and nonlinear least squares
Power and sample size calculations
There are no generally agreed methods for relating the number of observations versus the number of independent variables in the model. One rule of thumb suggested by Good and Hardin is
, where
is the sample size,
is the number of independent variables and
is the number of observations needed to reach the desired precision if the model had only one independent variable. For example, a researcher is building a linear regression model using a dataset that contains 1000 patients (
). If he decides that five observations are needed to precisely define a straight line (
), then the maximum number of independent variables his model can support is 4, because
.
Other methods
Although the parameters of a regression model are usually estimated using the method of least squares, other methods which have been used include:
 Bayesian methods, e.g. Bayesian linear regression
In statistics, Bayesian linear regression is an approach to linear regression in which the statistical analysis is undertaken within the context of Bayesian inference...
 Percentage regression, for situations where reducing percentage errors is deemed more appropriate.
 Least absolute deviations
Least absolute deviations , also known as Least Absolute Errors , Least Absolute Value , or the L1 norm problem, is a mathematical optimization technique similar to the popular least squares technique that attempts to find a function which closely approximates a set of data...
, which is more robust in the presence of outliers, leading to quantile regressionQuantile regression is a type of regression analysis used in statistics. Whereas the method of least squares results in estimates that approximate the conditional mean of the response variable given certain values of the predictor variables, quantile regression results in estimates approximating...
 Nonparametric regression
Nonparametric regression is a form of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data...
, requires a large number of observations and is computationally intensive
 Distance metric learning, which is learned by the search of a meaningful distance metric in a given input space.
Software
All major statistical software packages perform
least squaresThe method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. "Least squares" means that the overall solution minimizes the sum of the squares of the errors made in solving every...
regression analysis and inference.
Simple linear regressionIn statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model as...
and multiple regression using least squares can be done in some
spreadsheetA spreadsheet is a computer application that simulates a paper accounting worksheet. It displays multiple cells usually in a twodimensional matrix or grid consisting of rows and columns. Each cell contains alphanumeric text, numeric values or formulas...
applications and on some calculators. While many statistical software packages can perform various types of nonparametric and robust regression, these methods are less standardized; different software packages implement different methods, and a method with a given name may be implemented differently in different packages. Specialized regression software has been developed for use in fields such as survey analysis and neuroimaging.
See also
 Pearson productmoment correlation coefficient
In statistics, the Pearson productmoment correlation coefficient is a measure of the correlation between two variables X and Y, giving a value between +1 and −1 inclusive...
 Fraction of variance unexplained
In statistics, the fraction of variance unexplained in the context of a regression task is the fraction of variance of the regressand Y which cannot be explained, i.e., which is not correctly predicted, by the explanatory variables X....
 Segmented regression
Segmented regression is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented or piecewise regression analysis can also be performed on multivariate data by partitioning the various independent...
 Curve fitting
Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function...
 Kriging
Kriging is a group of geostatistical techniques to interpolate the value of a random field at an unobserved location from observations of its value at nearby locations....
(a linear least squares estimation algorithm)
 Forecasting
Forecasting is the process of making statements about events whose actual outcomes have not yet been observed. A commonplace example might be estimation for some variable of interest at some specified future date. Prediction is a similar, but more general term...
 Prediction interval
In statistical inference, specifically predictive inference, a prediction interval is an estimate of an interval in which future observations will fall, with a certain probability, given what has already been observed...
 Trend estimation
Trend estimation is a statistical technique to aid interpretation of data. When a series of measurements of a process are treated as a time series, trend estimation can be used to make and justify statements about tendencies in the data...
 Robust regression
In robust statistics, robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and nonparametric methods. Regression analysis seeks to find the effect of one or more independent variables upon a dependent variable...
 Modifiable areal unit problem
The modifiable areal unit problem is a source of statistical bias that can radically affect the results of statistical hypothesis tests. It affects results when pointbased measures of spatial phenomena are aggregated into districts. The resulting summary values are influenced by the choice of...
 Multivariate normal distribution
 Multivariate adaptive regression splines
Multivariate adaptive regression splines is a form of regression analysis introduced by Jerome Friedman in 1991. It is a nonparametric regression techniqueand can be seen as an extension of linear models that...
 Stepwise regression
In statistics, stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure...
 Local regression
LOESS, or LOWESS , is one of many "modern" modeling methods that build on "classical" methods, such as linear and nonlinear least squares regression. Modern regression methods are designed to address situations in which the classical procedures do not perform well or cannot be effectively applied...
Further reading
 William H. Kruskal
William Henry Kruskal was an American mathematician and statistician. He is best known for having formulated the Kruskal–Wallis oneway analysis of variance , a widelyused nonparametric statistical method.Kruskal was born in New York City to a successful fur wholesaler...
and Judith M. Tanur, ed. (1978), "Linear Hypotheses," International Encyclopedia of Statistics. Free Press, v. 1,
 Evan J. Williams, "I. Regression," pp. 523–41.
 Julian C. Stanley, "II. Analysis of Variance," pp. 541–554.
 Lindley, D.V. (1987). "Regression and correlation analysis," New Palgrave: A Dictionary of Economics, v. 4, pp. 120–23.
 Birkes, David and Dodge, Y.
Yadolah Dodge is an Iranian and Swiss statistician.His major contributions are in the theory of operational research, simulation and regression.He spent his early years in Abadan, Iran...
, Alternative Methods of Regression. ISBN 0471568813
 Chatfield, C. (1993) "Calculating Interval Forecasts," Journal of Business and Economic Statistics, 11. pp. 121–135.
 Draper, N.R. and Smith, H. (1998).Applied Regression Analysis Wiley Series in Probability and Statistics
 Fox, J. (1997). Applied Regression Analysis, Linear Models and Related Methods. Sage
 Hardle, W., Applied Nonparametric Regression (1990), ISBN 0521429501
 Meade, N. and T. Islam (1995) "Prediction Intervals for Growth Curve Forecasts" Journal of Forecasting, 14, pp. 413–430.
 N. Cressie (1996) Change of Support and the Modiable Areal Unit Problem. Geographical Systems 3:159–180.
 A.S. Fotheringham, C. Brunsdon, and M. Charlton. (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley.
 T. Strutz: Data Fitting and Uncertainty (A practical introduction to weighted least squares and beyond). Vieweg+Teubner, ISBN 9783834810229.
External links