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Encyclopedia > Least squares

In regression analysis, least squares, also known as ordinary least squares analysis, is a method for linear regression that determines the values of unknown quantities in a statistical model by minimizing the sum of the residuals (the difference between the predicted and observed values) squared. This method was first described by Carl Friedrich Gauss at the turn of the 19th century. Today, this method is available in most statistical software packages. The least-squares approach to regression analysis has been shown to be optimal in the sense that it satisfies the Gauss-Markov theorem. In statistics, regression analysis examines the relation of a dependent variable (response variable) to specified independent variables (explanatory variables). ... In statistics, linear regression is a regression method that models the relationship between a dependent variable Y, independent variables Xp, and a random term Îµ. The model can be written as where Î²1 is the intercept (constant term), the Î²is are the respective parameters of independent variables, and p is the... Johann Carl Friedrich Gauss or GauÃŸ ( ; Latin: ) (30 April 1777 â€“ 23 February 1855) was a German mathematician and scientist who contributed significantly to many fields, including number theory, analysis, differential geometry, geodesy, electrostatics, astronomy, and optics. ... Alternative meaning: Nineteenth Century (periodical) (18th century &#8212; 19th century &#8212; 20th century &#8212; more centuries) As a means of recording the passage of time, the 19th century was that century which lasted from 1801-1900 in the sense of the Gregorian calendar. ... This article is not about Gauss-Markov processes. ...

A related method is the least mean squares (LMS) method. It occurs when the number of measured data is 1 and the gradient descent method is used to minimize the squared residual. LMS is known to minimize the expectation of the squared residual, with the smallest number of operations per iteration). However, it requires a large number of iterations to converge. Least mean squares algorithms are used in adaptive filters to find the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal). ... Gradient descent is an optimization algorithm that approaches a local minimum of a function by taking steps proportional to the negative of the gradient (or the approximate gradient) of the function at the current point. ...

Furthermore, many other types of optimization problems can be expressed in a least squares form, by either minimizing energy or maximizing entropy. Claude Shannon In information theory, the Shannon entropy or information entropy is a measure of the uncertainty associated with a random variable. ...

In 1795, Carl Friedrich Gauss, at the age of 18, is credited with developing the fundamentals of the basis for least-squares analysis. However, as with many of his discoveries, he did not publish them. The strength of his method was demonstrated in 1801, when it was used to predict the future location of the newly discovered asteroid Ceres. Image File history File links Download high resolution version (576x738, 235 KB) Description: Ausschnitt aus einem GemÃ¤lde von C. F. Gauss Source: evtl. ... Image File history File links Download high resolution version (576x738, 235 KB) Description: Ausschnitt aus einem GemÃ¤lde von C. F. Gauss Source: evtl. ... Johann Carl Friedrich Gauss or GauÃŸ ( ; Latin: ) (30 April 1777 â€“ 23 February 1855) was a German mathematician and scientist who contributed significantly to many fields, including number theory, analysis, differential geometry, geodesy, electrostatics, astronomy, and optics. ... 1795 was a common year starting on Thursday (see link for calendar). ... Johann Carl Friedrich Gauss or GauÃŸ ( ; Latin: ) (30 April 1777 â€“ 23 February 1855) was a German mathematician and scientist who contributed significantly to many fields, including number theory, analysis, differential geometry, geodesy, electrostatics, astronomy, and optics. ... 1 Ceres (SEER eez) was the first asteroid to be discovered, with a diameter of 959. ...

On January 1st, 1801, the Italian astronomer Giuseppe Piazzi had discovered the asteroid Ceres and had been able to track its path for 40 days before it was lost in the glare of the sun. Based on this data, it was desired to determine the location of Ceres after it emerged from behind the sun without solving the complicated Kepler's nonlinear equations of planetary motion. The only predictions that successfully allowed the Hungarian astronomer Franz Xaver von Zach to relocate Ceres were those performed by the 24-year-old Gauss using least-squares analysis. January 1 is the first day of the calendar year in both the Julian and Gregorian calendars. ... The Union Jack, flag of the newly formed United Kingdom of Great Britain and Ireland. ... Giuseppe Piazzi. ... Johannes Keplers primary contributions to astronomy/astrophysics were his three laws of planetary motion. ... Franz Xaver, Baron Von Zach Baron Franz Xaver von Zach (Franz Xaver Freiherr von Zach) (June 4, 1754 - September 2, 1832) was an Austrian astronomer born at Bratislava. ...

However, Gauss did not publish the method until 1809, when it appeared in volume two of his work on celestial mechanics, Theoria Motus Corporum Coelestium in sectionibus conicis solem ambientium. Year 1809 (MDCCCIX) was a common year starting on Sunday (link will display the full calendar). ...

The idea of least-squares analysis was independently formulated by the Frenchman Adrien-Marie Legendre in 1805 and the American Robert Adrain in 1808. Adrien-Marie Legendre (September 18, 1752 â€“ January 10, 1833) was a French mathematician. ... 1805 was a common year starting on Tuesday (see link for calendar). ... Robert Adrain (September 30, 1775 - August 10, 1843) was a scientist and mathematician. ... Year 1808 (MDCCCVIII) was a leap year starting on Friday (link will display the full calendar) of the Gregorian calendar (or a leap year starting on Wednesday of the 12-day slower Julian calendar). ...

In 1829, Gauss was able to state that the least-squares approach to regression analysis is optimal in the sense that in a linear model where the errors have a mean of zero, are uncorrelated, and have equal variances, the best linear unbiased estimators of the coefficients is the least-squares estimators. This result is known as the Gauss-Markov theorem. Johann Wolfgang von Goethe 1829 was a common year starting on Thursday (see link for calendar). ... This article is not about Gauss-Markov processes. ...

## Problem statement

The objective consists of adjusting a model function to best fit a data set. The chosen model function has adjustable parameters. The data set consist of n points with . The model function has the form , where y is the dependent variable, are the independent variables, and are the model adjustable parameters. We wish to find the parameter values such that the model best fits the data according to a defined error criterion. The least sum square method minimizes the sum square error equation with respect to the adjustable parameters .

For an example, the data is height measurements over a surface. We choose to model the data by a plane with parameters for plane mean height, plane tip angle, and plane tilt angle. The model equation is then y = f(x1,x2) = a1 + a2x1 + a3x2, the independent variables are , and the adjustable parameters are .

## Solving the least squares problem

Least square optimization problems can be divided into linear and non-linear problems. The linear problem has a closed form solution. The optimization problem is said to be a linear optimization problem if the first order partial derivatives of S with respect to the parameters results in a set of equations that is linear in the parameter variables. The general, non-linear, unconstrained optimization problem has no closed form solution. In this case recursive methods, such as Newton's method, combined with the gradient descent method, or specialized methods for least squares analysis, such as the Gauss-Newton algorithm or the Levenberg-Marquardt algorithm can be used. In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set. ... In mathematics, Newtons method is a well-known algorithm for finding roots of equations in one or more dimensions. ... Gradient descent is an optimization algorithm that approaches a local minimum of a function by taking steps proportional to the negative of the gradient (or the approximate gradient) of the function at the current point. ... In mathematics, the Gauss-Newton algorithm is used to solve nonlinear least squares problems. ... The Levenberg-Marquardt algorithm provides a numerical solution to the mathematical problem of minimizing a function, generally nonlinear, over a space of parameters of the function. ...

## Least squares and regression analysis

In regression analysis, one replaces the relation In statistics, regression analysis examines the relation of a dependent variable (response variable) to specified independent variables (explanatory variables). ...

by

where the noise term ε is a random variable with mean zero. Note that we are assuming that the x values are exact, and all the errors are in the y values. Again, we distinguish between linear regression, in which case the function f is linear in the parameters to be determined (e.g., f(x) = ax2 + bx + c), and nonlinear regression. As before, linear regression is much simpler than nonlinear regression. (It is tempting to think that the reason for the name linear regression is that the graph of the function f(x) = ax + b is a line. But fitting a curve like f(x) = ax2 + bx + c when estimating a, b, and c by least squares, is an instance of linear regression because the vector of least-square estimates of a, b, and c is a linear transformation of the vector whose components are f(xi) + εi. In probability theory, a random variable is a quantity whose values are random and to which a probability distribution is assigned. ... In statistics, linear regression is a regression method that models the relationship between a dependent variable Y, independent variables Xp, and a random term Îµ. The model can be written as where Î²1 is the intercept (constant term), the Î²is are the respective parameters of independent variables, and p is the... dataset with approximating polynomials Nonlinear regression in statistics is the problem of fitting a model to multidimensional x,y data, where f is a nonlinear function of x with parameters Î¸. In general, there is no algebraic expression for the best-fitting parameters, as there is in linear regression. ... In mathematics, a linear transformation (also called linear map or linear operator) is a function between two vector spaces that preserves the operations of vector addition and scalar multiplication. ...

### Parameter estimates

By recognizing that the regression model is a system of linear equations we can express the model using data matrix X, target vector Y and parameter vector δ. The ith row of X and Y will contain the x and y value for the ith data sample. Then the model can be written as

which when using pure matrix notation becomes

where ε is normally distributed with expected value 0 (i.e., a column vector of 0s) and variance σ2 In, where In is the n×n identity matrix.

The least-squares estimator for δ is In parametric statistics, the least-squares estimator is often used to estimate the coefficients of a linear regression. ...

(where XT is the transpose of X) and the sum of squares of residuals is

One of the properties of least-squares is that the matrix is the orthogonal projection of Y onto the column space of X.

The fact that the matrix X(XTX)−1XT is a symmetric idempotent matrix is incessantly relied on in proofs of theorems. The linearity of as a function of the vector Y, expressed above by saying In linear algebra, a symmetric matrix is a matrix that is its own transpose. ... In mathematics, an idempotent element is an element which, intuitively, leaves something unchanged. ...

is the reason why this is called "linear" regression. Nonlinear regression uses nonlinear methods of estimation.

The matrix In − X (XT X)−1 XT that appears above is a symmetric idempotent matrix of rank n − 2. Here is an example of the use of that fact in the theory of linear regression. The finite-dimensional spectral theorem of linear algebra says that any real symmetric matrix M can be diagonalized by an orthogonal matrix G, i.e., the matrix GMG is a diagonal matrix. If the matrix M is also idempotent, then the diagonal entries in GMG must be idempotent numbers. Only two real numbers are idempotent: 0 and 1. So In − X(XTX) -1XT, after diagonalization, has n − 2 1s and two 0s on the diagonal. That is most of the work in showing that the sum of squares of residuals has a chi-square distribution with n−2 degrees of freedom. In mathematics, particularly linear algebra and functional analysis, the spectral theorem is any of a number of results about linear operators or about matrices. ... Linear algebra is the branch of mathematics concerned with the study of vectors, vector spaces (also called linear spaces), linear maps (also called linear transformations), and systems of linear equations. ... In matrix theory, a real orthogonal matrix is a square matrix Q whose transpose is its inverse: // Overview An orthogonal matrix is the real specialization of a unitary matrix, and thus always a normal matrix. ... In probability theory and statistics, the chi-square distribution (also chi-squared or Ï‡2  distribution) is one of the theoretical probability distributions most widely used in inferential statistics, i. ...

Regression parameters can also be estimated by Bayesian methods. This has the advantages that Bayesian refers to probability and statistics -- either methods associated with the Reverend Thomas Bayes (ca. ...

• confidence intervals can be produced for parameter estimates without the use of asymptotic approximations,
• prior information can be incorporated into the analysis.

Suppose that in the linear regression In this diagram, the bars represent observation means and the red lines represent the confidence intervals surrounding them. ...

we know from domain knowledge that alpha can only take one of the values {−1, +1} but we do not know which. We can build this information into the analysis by choosing a prior for alpha which is a discrete distribution with a probability of 0.5 on −1 and 0.5 on +1. The posterior for alpha will also be a discrete distribution on {−1, +1}, but the probability weights will change to reflect the evidence from the data.

In modern computer applications, the actual value of β is calculated using the QR decomposition or slightly more fancy methods when XTX is near singular. The code for the MATLAB function is an excellent example of a robust method. In linear algebra, the QR decomposition (also called the QR factorization) of a matrix is a decomposition of the matrix into an orthogonal and a triangular matrix. ... MATLAB is a numerical computing environment and programming language. ...

#### Summarizing the data

We sum the observations, the squares of the Xs and the products XY to obtain the following quantities.

#### Estimating beta (the slope)

We use the summary statistics above to calculate , the estimate of β.

#### Estimating alpha (the intercept)

We use the estimate of β and the other statistics to estimate α by:

A consequence of this estimate is that the regression line will always pass through the "center" .

## Limitations

Least squares estimation for linear models is notoriously non-robust to outliers. If the distribution of the outliers is skewed, the estimates can be biased. In the presence of any outliers, the least squares estimates are inefficient and can be extremely so. When outliers occur in the data, methods of robust regression are more appropriate. In robust statistics, robust regression is a form of regression analysis designed to circumvent the limitations of traditional parametric and non-parametric methods. ... Results from FactBites:

 Least Squares Estimation Curve Fitting Program to download. Nonlinear Weighted Least Squares Regression Analysis. ... (1450 words) Least Squares Estimation Curve Fitting Program to download. This form enables applying complicated curves that are not a graph of any function, applying complicated curve equations from which none of variables can be derived as well as transforming equations to be always computable. On the basis of input errors, the chi-sqr parameter and its standard deviation is calculated (chi-sqr expected value equals the number of degrees of freedom).
 4.1.4.1. Linear Least Squares Regression (883 words) The "method of least squares" that is used to obtain parameter estimates was independently developed in the late 1700's and the early 1800's by the mathematicians Karl Friedrich Gauss, Adrien Marie Legendre and (possibly) Robert Adrain [Stigler (1978)] [Harter (1983)] [Stigler (1986)] working in Germany, France and America, respectively. In the least squares method the unknown parameters are estimated by minimizing the sum of the squared deviations between the data and the model. The estimates of the unknown parameters obtained from linear least squares regression are the optimal estimates from a broad class of possible parameter estimates under the usual assumptions used for process modeling.
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