Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction
, where its use has substantial overlap with the field of machine learning
. Second, in some situations regression analysis can be used to infer causal relationships
between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset. To use regressions for prediction or to infer causal relationships, respectively, a researcher must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. The latter is especially important when researchers hope to estimate causal relationships using observational data
The earliest form of regression was the method of least squares
, which was published by Legendre
and by Gauss
Legendre and Gauss both applied the method to the problem of determining, from astronomical observations, the orbits of bodies about the Sun (mostly comets, but also later the then newly discovered minor planets). Gauss published a further development of the theory of least squares in 1821,
including a version of the Gauss–Markov theorem
The term "regression" was coined by Francis Galton
in the 19th 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 mean
For Galton, regression had only this biological meaning,
but his work was later extended by Udny Yule
and Karl Pearson
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.
In the 1950s and 1960s, economists used electromechanical desk "calculators" to calculate regressions. Before 1970, it sometimes took up to 24 hours to receive the result from one regression.
Regression methods continue to be an area of active research. In recent decades, new methods have been developed for robust regression
, regression involving correlated responses such as time series
and growth curves
, regression in which the predictor (independent variable) or response variables are curves, images, graphs, or other complex data objects, regression methods accommodating various types of missing data, nonparametric regression
methods for regression, regression in which the predictor variables are measured with error, regression with more predictor variables than observations, and causal inference
In practice, researchers first select a model they would like to estimate and then use their chosen method (e.g., ordinary least squares
) to estimate the parameters of that model. Regression models involve the following components:
- The unknown parameters, often denoted as a scalar or vector .
- The independent variables, which are observed in data and are often denoted as a vector (where denotes a row of data).
- The dependent variable, which are observed in data and often denoted using the scalar .
- The error terms, which are not directly observed in data and are often denoted using the scalar .
Most regression models propose that
is a function of
and , with
representing an additive error term
that may stand in for un-modeled determinants of
or random statistical noise:
The researchers' goal is to estimate the function
that most closely fits the data. To carry out regression analysis, the form of the function must be specified. Sometimes the form of this function is based on knowledge about the relationship between
that does not rely on the data. If no such knowledge is available, a flexible or convenient form for is chosen. For example, a simple univariate regression may propose
, suggesting that the researcher believes
to be a reasonable approximation for the statistical process generating the data.
Once researchers determine their preferred statistical model
, different forms of regression analysis provide tools to estimate the parameters . For example, least squares
(including its most common variant, ordinary least squares
) finds the value of that minimizes the sum of squared errors
. A given regression method will ultimately provide an estimate of , usually denoted to distinguish the estimate from the true (unknown) parameter value that generated the data. Using this estimate, the researcher can then use the fitted value
for prediction or to assess the accuracy of the model in explaining the data. Whether the researcher is intrinsically interested in the estimate or the predicted value
will depend on context and their goals. As described in ordinary least squares
, least squares is widely used because the estimated function
approximates the conditional expectation
However, alternative variants (e.g., least absolute deviations
or quantile regression
) are useful when researchers want to model other functions
It is important to note that there must be sufficient data to estimate a regression model. For example, suppose that a researcher has access to
rows of data with one dependent and two independent variables:
. Suppose further that the researcher wants to estimate a bivariate linear model via least squares
. If the researcher only has access to
data points, then they could find infinitely many combinations
that explain the data equally well: any combination can be chosen that satisfies
, all of which lead to
and are therefore valid solutions that minimize the sum of squared residuals
. To understand why there are infinitely many options, note that the system of
equations is to be solved for 3 unknowns, which makes the system underdetermined
. Alternatively, one can visualize infinitely many 3-dimensional planes that go through
More generally, to estimate a least squares
model with distinct parameters, one must have
distinct data points. If
, then there does not generally exist a set of parameters that will perfectly fit the data. The quantity
appears often in regression analysis, and is referred to as the degrees of freedom
in the model. Moreover, to estimate a least squares model, the independent variables
must be linearly independent
: one must not
be able to reconstruct any of the independent variables by adding and multiplying the remaining independent variables. As discussed in ordinary least squares
, this condition ensures that
is an invertible matrix
and therefore that a unique solution exists.
By itself, a regression is simply a calculation using the data. In order to interpret the output of a regression as a meaningful statistical quantity that measures real-world relationships, researchers often rely on a number of classical assumptions
. These often include:
- The sample is representative of the population at large.
- The independent variables are measured with no error.
- Deviations from the model have an expected value of zero, conditional on covariates:
- The variance of the residuals is constant across observations (homoscedasticity).
- The residuals are uncorrelated with one another. Mathematically, the variance–covariance matrix of the errors is diagonal.
A handful of conditions are sufficient for the least-squares estimator to possess desirable properties: in particular, the Gauss–Markov
assumptions imply that the parameter estimates will be unbiased
, and efficient
in the class of linear unbiased estimators. Practitioners have developed a variety of methods to maintain some or all of these desirable properties in real-world settings, because these classical assumptions are unlikely to hold exactly. For example, modeling errors-in-variables
can lead to reasonable estimates independent variables are measured with errors. Heteroscedasticity-consistent standard errors
allow the variance of
to change across values of
. Correlated errors that exist within subsets of the data or follow specific patterns can be handled using clustered standard errors, geographic weighted regression
, or Newey–West
standard errors, among other techniques. When rows of data correspond to locations in space, the choice of how to model
within geographic units can have important consequences.
The subfield of econometrics
is largely focused on developing techniques that allow researchers to make reasonable real-world conclusions in real-world settings, where classical assumptions do not hold exactly.
In linear regression, the model specification is that the dependent variable,
is a linear combination
of the parameters
(but need not be linear in the independent variables
). For example, in simple linear regression
for modeling data points there is one independent variable:
, and two parameters,
In multiple linear regression, there are several independent variables or functions of independent variables.
Adding a term in
to the preceding regression gives:
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
In both cases,
is an error term and the subscript indexes a particular observation.
Returning our attention to the straight line case: Given a random sample from the population, we estimate the population parameters and obtain the sample linear regression model:
, 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 squares
. This method obtains parameter estimates that minimize the sum of squared residuals
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,
Illustration of linear regression on a data set.
In the case of simple regression, the formulas for the least squares estimates are
where is the mean
(average) of the values and is the mean of the values.
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 denominator is the sample size reduced by the number of model parameters estimated from the same data,
if an intercept is used.
In this case,
so the denominator is
General linear model
In the more general multiple regression model, there are independent variables:
is the -th observation on the -th independent variable. If the first independent variable takes the value 1 for all ,
is called the regression intercept
The least squares parameter estimates are obtained from normal equations. The residual can be written as
The normal equations are
In matrix notation, the normal equations are written as
, the element of the column vector
, and the element of
. The solution is
Once a regression model has been constructed, it may be important to confirm the goodness of fit
of the model and the statistical significance
of the estimated parameters. Commonly used checks of goodness of fit include the R-squared
, analyses of the pattern of residuals
and hypothesis testing. Statistical significance can be checked by an F-test
of the overall fit, followed by t-tests
of individual parameters.
Interpretations of these diagnostic tests rest heavily on the model's assumptions. Although examination of the residuals can be used to invalidate a model, the results of a t-test
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 theorem
can be invoked such that hypothesis testing may proceed using asymptotic approximations.
Limited dependent variables
The response variable may be non-continuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with least-squares linear regression, the model is called the linear probability model
. Nonlinear models for binary dependent variables include the probit
and logit model
. The multivariate probit
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 logit
. For ordinal variables
with more than two values, there are the ordered logit
and ordered probit
models. Censored regression models
may be used when the dependent variable is only sometimes observed, and Heckman correction
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 correlation
(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 regression
or the negative binomial
model may be used.
Interpolation and extrapolation
In the middle, the interpolated straight line represents the best balance between the points above and below this line. The dotted lines represent the two extreme lines. The first curves represent the estimated values. The outer curves represent a prediction for a new measurement.
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 model-fitting is known informally as interpolation
. Prediction outside
this range of the data is known as extrapolation
. 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.
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 modeling errors that may be made: in particular, the assumption of a particular form for the relation between Y
. 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. Best-practice advice here
is that a linear-in-variables and linear-in-parameters 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).
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 conjectured by Good and Hardin is
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 the researcher decides that five observations are needed to precisely define a straight line (
), then the maximum number of independent variables the model can support is 4, because
Although the parameters of a regression model are usually estimated using the method of least squares, other methods which have been used include:
All major statistical software packages perform least squares
regression analysis and inference. Simple linear regression
and multiple regression using least squares can be done in some spreadsheet
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.
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