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In statistical modeling, **regression analysis** is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared distances between the true data and that line (or hyperplane). For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis^{ [1] }) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression).

- History
- Regression model
- Underlying assumptions
- Linear regression
- General linear model
- Diagnostics
- Limited dependent variables
- Nonlinear regression
- Interpolation and extrapolation
- Power and sample size calculations
- Other methods
- Software
- See also
- References
- Further reading
- External links

Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, 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.^{ [2] }^{ [3] }

The earliest form of regression was the method of least squares, which was published by Legendre in 1805,^{ [4] } and by Gauss in 1809.^{ [5] } 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,^{ [6] } including a version of the Gauss–Markov theorem.

The term "regression" was coined by Francis Galton 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 mean).^{ [7] }^{ [8] } For Galton, regression had only this biological meaning,^{ [9] }^{ [10] } but his work was later extended by Udny Yule and Karl Pearson to a more general statistical context.^{ [11] }^{ [12] } 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.^{ [13] }^{ [14] }^{ [15] } 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.^{ [16] }

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, Bayesian 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.

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 .

In various fields of application, different terminologies are used in place of dependent and independent variables.

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 and 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 her goals. As described in ordinary least squares, least squares is widely used because the estimated function approximates the conditional expectation .^{ [5] } 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 fixed points.

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 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, consistent, 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.^{ [17] }^{ [18] } 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, and :

- straight line:

In multiple linear regression, there are several independent variables or functions of independent variables.

Adding a term in 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.

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:

The residual, , 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, SSR:

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 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, for regressors or if an intercept is used.^{ [19] } In this case, so the denominator is .

The standard errors 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 intervals and conduct hypothesis tests about the population parameters.

In the more general multiple regression model, there are independent variables:

where is the -th observation on the -th independent variable. If the first independent variable takes the value 1 for all , , then 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

where the element of is , the element of the column vector is , and the element of is . Thus is , is , and is . 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 or F-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, which are response variables that are categorical variables or are variables constrained to fall only in a certain range, often arise in econometrics.

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.

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 non-linear least squares.

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 ^{[ citation needed ]} 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.^{ [21] }

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* 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. Best-practice advice here^{[ citation needed ]} 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).

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 , 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.^{ [22] } 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:

- Bayesian methods, e.g. Bayesian linear regression
- Percentage regression, for situations where reducing
*percentage*errors is deemed more appropriate.^{ [23] } - Least absolute deviations, which is more robust in the presence of outliers, leading to quantile regression
- Nonparametric regression, requires a large number of observations and is computationally intensive
- Scenario optimization, leading to interval predictor models
- Distance metric learning, which is learned by the search of a meaningful distance metric in a given input space.
^{ [24] }

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.

- Anscombe's quartet
- Curve fitting
- Estimation theory
- Forecasting
- Fraction of variance unexplained
- Function approximation
- Generalized linear models
- Kriging (a linear least squares estimation algorithm)
- Local regression
- Modifiable areal unit problem
- Multivariate adaptive regression splines
- Multivariate normal distribution
- Pearson product-moment correlation coefficient
- Quasi-variance
- Prediction interval
- Regression validation
- Robust regression
- Segmented regression
- Signal processing
- Stepwise regression
- Trend estimation

The method of **least squares** is a standard approach in regression analysis to approximate the solution of overdetermined systems by minimizing the sum of the squares of the residuals made in the results of every single equation.

In statistics, the **Gauss–Markov theorem** states that the ordinary least squares (OLS) estimator has the lowest sampling variance within the class of linear unbiased estimators, if the errors in the linear regression model are uncorrelated, have equal variances and expectation value of zero. The errors do not need to be normal, nor do they need to be independent and identically distributed. The requirement that the estimator be unbiased cannot be dropped, since biased estimators exist with lower variance. See, for example, the James–Stein estimator or ridge regression.

In statistics, **Deming regression**, named after W. Edwards Deming, is an errors-in-variables model which tries to find the line of best fit for a two-dimensional dataset. It differs from the simple linear regression in that it accounts for errors in observations on both the *x*- and the *y*- axis. It is a special case of total least squares, which allows for any number of predictors and a more complicated error structure.

In statistics, **nonlinear regression** is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations.

In statistics, the **coefficient of determination**, denoted *R*^{2} or *r*^{2} and pronounced "R squared", is the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

In statistics, econometrics, epidemiology and related disciplines, the method of **instrumental variables** (**IV**) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term, in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in the explanatory variable but has no independent effect on the dependent variable, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable.

In statistics, **omitted-variable bias** (**OVB**) occurs when a statistical model leaves out one or more relevant variables. The bias results in the model attributing the effect of the missing variables to the estimated effects of the included variables.

In statistics, **ordinary least squares** (**OLS**) is a type of linear least squares method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable in the given dataset and those predicted by the linear function.

In statistics, **simple linear regression** is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable and finds a linear function that, as accurately as possible, predicts the dependent variable values as a function of the independent variables. The adjective *simple* refers to the fact that the outcome variable is related to a single predictor.

In statistics, a **fixed effects model** is a statistical model in which the model parameters are fixed or non-random quantities. This is in contrast to random effects models and mixed models in which all or some of the model parameters are considered as random variables. In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population. Generally, data can be grouped according to several observed factors. The group means could be modeled as fixed or random effects for each grouping. In a fixed effects model each group mean is a group-specific fixed quantity.

In statistics, **generalized least squares** (**GLS**) is a technique for estimating the unknown parameters in a linear regression model when there is a certain degree of correlation between the residuals in a regression model. In these cases, ordinary least squares and weighted least squares can be statistically inefficient, or even give misleading inferences. GLS was first described by Alexander Aitken in 1934.

The topic of **heteroscedasticity-consistent** (**HC**) **standard errors** arises in statistics and econometrics in the context of linear regression and time series analysis. These are also known as **Eicker–Huber–White standard errors**, to recognize the contributions of Friedhelm Eicker, Peter J. Huber, and Halbert White.

In statistics, the **variance inflation factor** (**VIF**) is the quotient of the variance in a model with multiple terms by the variance of a model with one term alone. It quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance of an estimated regression coefficient is increased because of collinearity.

**Non-linear least squares** is the form of least squares analysis used to fit a set of *m* observations with a model that is non-linear in *n* unknown parameters (*m* ≥ *n*). It is used in some forms of nonlinear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations. There are many similarities to linear least squares, but also some significant differences. In economic theory, the non-linear least squares method is applied in (i) the probit regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box-Cox transformed regressors.

In statistics, **principal component regression** (**PCR**) is a regression analysis technique that is based on principal component analysis (PCA). More specifically, PCR is used for estimating the unknown regression coefficients in a standard linear regression model.

In statistics, a **sum of squares due to lack of fit**, or more tersely a **lack-of-fit sum of squares**, is one of the components of a partition of the sum of squares of residuals in an analysis of variance, used in the numerator in an F-test of the null hypothesis that says that a proposed model fits well. The other component is the **pure-error sum of squares**.

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 *n*th 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*). Although *polynomial regression* fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(*y* | *x*) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression.

In statistics, **errors-in-variables models** or **measurement error models** are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses.

**Linear least squares** (**LLS**) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods.

In statistics, **linear regression** is a linear approach to modeling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called **multiple linear regression**. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

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Wikimedia Commons has media related to . Regression analysis |

- Hazewinkel, Michiel, ed. (2001) [1994], "Regression analysis",
*Encyclopedia of Mathematics*, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 978-1-55608-010-4 - Earliest Uses: Regression – basic history and references
- Regression of Weakly Correlated Data – how linear regression mistakes can appear when Y-range is much smaller than X-range

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