Degrees of freedom ( statistics ) - wikipedia Degrees of freedom ( statistics ) For other uses, see Degrees of freedom. In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. The number of independent ways by which a dynamic system can move, without violating any constraint imposed on it, is called number of degrees of freedom. In other words, the number of degrees of freedom can be defined as the minimum number of independent coordinates that can specify the position of the system completely. Estimates of statistical parameters can be based upon different amounts of information or data. The number of independent pieces of information that go into the estimate of a parameter are called the degrees of freedom. In general, the degrees of freedom of an estimate of a parameter are equal to the number of independent scores that go into the estimate minus the number of parameters used as intermediate steps in the estimation of the parameter itself ( e.g. the sample variance has N − 1 degrees of freedom, since it is computed from N random scores minus the only 1 parameter estimated as intermediate step, which is the sample mean ). Mathematically, degrees of freedom is the number of dimensions of the domain of a random vector, or essentially the number of `` free '' components ( how many components need to be known before the vector is fully determined ). The term is most often used in the context of linear models ( linear regression, analysis of variance ), where certain random vectors are constrained to lie in linear subspaces, and the number of degrees of freedom is the dimension of the subspace. The degrees of freedom are also commonly associated with the squared lengths ( or `` sum of squares '' of the coordinates ) of such vectors, and the parameters of chi - squared and other distributions that arise in associated statistical testing problems. While introductory textbooks may introduce degrees of freedom as distribution parameters or through hypothesis testing, it is the underlying geometry that defines degrees of freedom, and is critical to a proper understanding of the concept. Walker ( 1940 ) has stated this succinctly as `` the number of observations minus the number of necessary relations among these observations. '' Contents 1 History 2 Notation 3 Of random vectors 3.1 Of residuals 4 In linear models 5 In analysis of variance ( ANOVA ) 6 In probability distributions 7 In nonparametric regression 7.1 Regression effective degrees of freedom 7.2 Residual effective degrees of freedom 7.3 General 7.4 Other formulations 7.4. 1 Alternative 8 See also 9 References 10 Further reading 11 External links History Although the basic concept of degrees of freedom was recognized as early as 1821 in the work of astronomer and mathematician Carl Friedrich Gauss, its modern definition and usage was first elaborated by English statistician William Sealy Gosset in his 1908 Biometrika article `` The Probable Error of a Mean '', published under the pen name `` Student ''. While Gosset did not actually use the term ' degrees of freedom ', he explained the concept in the course of developing what became known as Student 's t - distribution. The term itself was popularized by English statistician and biologist Ronald Fisher, beginning with his 1922 work on chi squares. Notation In equations, the typical symbol for degrees of freedom is ν ( lowercase Greek letter nu ). In text and tables, the abbreviation `` d.f. '' is commonly used. R.A. Fisher used n to symbolize degrees of freedom but modern usage typically reserves n for sample size. Of random vectors Geometrically, the degrees of freedom can be interpreted as the dimension of certain vector subspaces. As a starting point, suppose that we have a sample of independent normally distributed observations, X 1,..., X n. ( displaystyle X_ ( 1 ), dots, X_ ( n ). , ) This can be represented as an n - dimensional random vector : ( X 1 ⋮ X n ). ( displaystyle ( \begin ( pmatrix ) X_ ( 1 ) \vdots X_ ( n ) end ( pmatrix ) ). ) Since this random vector can lie anywhere in n - dimensional space, it has n degrees of freedom. Now, let X  ̄ ( displaystyle ( \bar ( X ) ) ) be the sample mean. The random vector can be decomposed as the sum of the sample mean plus a vector of residuals : ( X 1 ⋮ X n ) = X  ̄ ( 1 ⋮ 1 ) + ( X 1 − X  ̄ ⋮ X n − X  ̄ ). ( displaystyle ( \begin ( pmatrix ) X_ ( 1 ) \vdots X_ ( n ) end ( pmatrix ) ) = ( \bar ( X ) ) ( \begin ( pmatrix ) 1 \vdots 1 end ( pmatrix ) ) + ( \begin ( pmatrix ) X_ ( 1 ) - ( \bar ( X ) ) \vdots X_ ( n ) - ( \bar ( X ) ) end ( pmatrix ) ). ) The first vector on the right - hand side is constrained to be a multiple of the vector of 1 's, and the only free quantity is X  ̄ ( displaystyle ( \bar ( X ) ) ). It therefore has 1 degree of freedom. The second vector is constrained by the relation ∑ i = 1 n ( X i − X  ̄ ) = 0 ( displaystyle sum _ ( i = 1 ) ^ ( n ) ( X_ ( i ) - ( \bar ( X ) ) ) = 0 ). The first n − 1 components of this vector can be anything. However, once you know the first n − 1 components, the constraint tells you the value of the nth component. Therefore, this vector has n − 1 degrees of freedom. Mathematically, the first vector is the orthogonal, or least - squares, projection of the data vector onto the subspace spanned by the vector of 1 's. The 1 degree of freedom is the dimension of this subspace. The second residual vector is the least - squares projection onto the ( n − 1 ) - dimensional orthogonal complement of this subspace, and has n − 1 degrees of freedom. In statistical testing applications, often one is n't directly interested in the component vectors, but rather in their squared lengths. In the example above, the residual sum - of - squares is ∑ i = 1 n ( X i − X  ̄ ) 2 = ∥ X 1 − X  ̄ ⋮ X n − X  ̄ ∥ 2. ( displaystyle sum _ ( i = 1 ) ^ ( n ) ( X_ ( i ) - ( \bar ( X ) ) ) ^ ( 2 ) = ( \begin ( Vmatrix ) X_ ( 1 ) - ( \bar ( X ) ) \vdots X_ ( n ) - ( \bar ( X ) ) end ( Vmatrix ) ) ^ ( 2 ). ) If the data points X i ( displaystyle X_ ( i ) ) are normally distributed with mean 0 and variance σ 2 ( displaystyle sigma ^ ( 2 ) ), then the residual sum of squares has a scaled chi - squared distribution ( scaled by the factor σ 2 ( displaystyle sigma ^ ( 2 ) ) ), with n − 1 degrees of freedom. The degrees - of - freedom, here a parameter of the distribution, can still be interpreted as the dimension of an underlying vector subspace. Likewise, the one - sample t - test statistic, n ( X  ̄ − μ 0 ) ∑ i = 1 n ( X i − X  ̄ ) 2 / ( n − 1 ) ( displaystyle ( \frac ( ( sqrt ( n ) ) ( ( \bar ( X ) ) - mu _ ( 0 ) ) ) ( sqrt ( sum limits _ ( i = 1 ) ^ ( n ) ( X_ ( i ) - ( \bar ( X ) ) ) ^ ( 2 ) / ( n - 1 ) ) ) ) ) follows a Student 's t distribution with n − 1 degrees of freedom when the hypothesized mean μ 0 ( displaystyle mu _ ( 0 ) ) is correct. Again, the degrees - of - freedom arises from the residual vector in the denominator. Of residuals Further information : Residuals ( statistics ) A common way to think of degrees of freedom is as the number of independent pieces of information available to estimate another piece of information. More concretely, the number of degrees of freedom is the number of independent observations in a sample of data that are available to estimate a parameter of the population from which that sample is drawn. For example, if we have two observations, when calculating the mean we have two independent observations ; however, when calculating the variance, we have only one independent observation, since the two observations are equally distant from the mean. In fitting statistical models to data, the vectors of residuals are constrained to lie in a space of smaller dimension than the number of components in the vector. That smaller dimension is the number of degrees of freedom for error. Example Perhaps the simplest example is this. Suppose X 1,..., X n ( displaystyle X_ ( 1 ), dots, X_ ( n ) ) are random variables each with expected value μ, and let X  ̄ n = X 1 + ⋯ + X n n ( displaystyle ( overline ( X ) ) _ ( n ) = ( X_ ( 1 ) + cdots + X_ ( n ) over n ) ) be the `` sample mean. '' Then the quantities X i − X  ̄ n ( displaystyle X_ ( i ) - ( overline ( X ) ) _ ( n ) , ) are residuals that may be considered estimates of the errors X − μ. The sum of the residuals ( unlike the sum of the errors ) is necessarily 0. If one knows the values of any n − 1 of the residuals, one can thus find the last one. That means they are constrained to lie in a space of dimension n − 1. One says that `` there are n − 1 degrees of freedom for errors. '' An example which is only slightly less simple is that of least squares estimation of a and b in the model Y i = a + b x i + e i for i = 1,..., n ( displaystyle Y_ ( i ) = a + bx_ ( i ) + e_ ( i ) ( \text ( for ) ) i = 1, dots, n ) where x is given, but e and hence Y are random. Let a ^ ( displaystyle ( widehat ( a ) ) ) and b ^ ( displaystyle ( widehat ( b ) ) ) be the least - squares estimates of a and b. Then the residuals e ^ i = y i − ( a ^ + b ^ x i ) ( displaystyle ( widehat ( e ) ) _ ( i ) = y_ ( i ) - ( ( widehat ( a ) ) + ( widehat ( b ) ) x_ ( i ) ) , ) are constrained to lie within the space defined by the two equations e ^ 1 + ⋯ + e ^ n = 0, ( displaystyle ( widehat ( e ) ) _ ( 1 ) + cdots + ( widehat ( e ) ) _ ( n ) = 0, , ) x 1 e ^ 1 + ⋯ + x n e ^ n = 0. ( displaystyle x_ ( 1 ) ( widehat ( e ) ) _ ( 1 ) + cdots + x_ ( n ) ( widehat ( e ) ) _ ( n ) = 0. , ) One says that there are n − 2 degrees of freedom for error. Notationally, the capital letter Y is used in specifying the model, while lower - case y in the definition of the residuals ; that is because the former are hypothesized random variables and the latter are actual data. We can generalise this to multiple regression involving p parameters and covariates ( e.g. p − 1 predictors and one mean ), in which case the cost in degrees of freedom of the fit is p. In linear models The demonstration of the t and chi - squared distributions for one - sample problems above is the simplest example where degrees - of - freedom arise. However, similar geometry and vector decompositions underlie much of the theory of linear models, including linear regression and analysis of variance. An explicit example based on comparison of three means is presented here ; the geometry of linear models is discussed in more complete detail by Christensen ( 2002 ). Suppose independent observations are made for three populations, X 1,..., X n ( displaystyle X_ ( 1 ), ldots, X_ ( n ) ), Y 1,..., Y n ( displaystyle Y_ ( 1 ), ldots, Y_ ( n ) ) and Z 1,..., Z n ( displaystyle Z_ ( 1 ), ldots, Z_ ( n ) ). The restriction to three groups and equal sample sizes simplifies notation, but the ideas are easily generalized. The observations can be decomposed as X i = M  ̄ + ( X  ̄ − M  ̄ ) + ( X i − X  ̄ ) Y i = M  ̄ + ( Y  ̄ − M  ̄ ) + ( Y i − Y  ̄ ) Z i = M  ̄ + ( Z  ̄ − M  ̄ ) + ( Z i − Z  ̄ ) ( displaystyle ( \begin ( aligned ) X_ ( i ) & = ( \bar ( M ) ) + ( ( \bar ( X ) ) - ( \bar ( M ) ) ) + ( X_ ( i ) - ( \bar ( X ) ) ) Y_ ( i ) & = ( \bar ( M ) ) + ( ( \bar ( Y ) ) - ( \bar ( M ) ) ) + ( Y_ ( i ) - ( \bar ( Y ) ) ) Z_ ( i ) & = ( \bar ( M ) ) + ( ( \bar ( Z ) ) - ( \bar ( M ) ) ) + ( Z_ ( i ) - ( \bar ( Z ) ) ) end ( aligned ) ) ) where X  ̄, Y  ̄, Z  ̄ ( displaystyle ( \bar ( X ) ), ( \bar ( Y ) ), ( \bar ( Z ) ) ) are the means of the individual samples, and M  ̄ = ( X  ̄ + Y  ̄ + Z  ̄ ) / 3 ( displaystyle ( \bar ( M ) ) = ( ( \bar ( X ) ) + ( \bar ( Y ) ) + ( \bar ( Z ) ) ) / 3 ) is the mean of all 3n observations. In vector notation this decomposition can be written as ( X 1 ⋮ X n Y 1 ⋮ Y n Z 1 ⋮ Z n ) = M  ̄ ( 1 ⋮ 1 1 ⋮ 1 1 ⋮ 1 ) + ( X  ̄ − M  ̄ ⋮ X  ̄ − M  ̄ Y  ̄ − M  ̄ ⋮ Y  ̄ − M  ̄ Z  ̄ − M  ̄ ⋮ Z  ̄ − M  ̄ ) + ( X 1 − X  ̄ ⋮ X n − X  ̄ Y 1 − Y  ̄ ⋮ Y n − Y  ̄ Z 1 − Z  ̄ ⋮ Z n − Z  ̄ ). ( displaystyle ( \begin ( pmatrix ) X_ ( 1 ) \vdots X_ ( n ) Y_ ( 1 ) \vdots Y_ ( n ) Z_ ( 1 ) \vdots Z_ ( n ) end ( pmatrix ) ) = ( \bar ( M ) ) ( \begin ( pmatrix ) 1 \vdots 1 1 \vdots 1 1 \vdots 1 end ( pmatrix ) ) + ( \begin ( pmatrix ) ( \bar ( X ) ) - ( \bar ( M ) ) \vdots ( \bar ( X ) ) - ( \bar ( M ) ) ( \bar ( Y ) ) - ( \bar ( M ) ) \vdots ( \bar ( Y ) ) - ( \bar ( M ) ) ( \bar ( Z ) ) - ( \bar ( M ) ) \vdots ( \bar ( Z ) ) - ( \bar ( M ) ) end ( pmatrix ) ) + ( \begin ( pmatrix ) X_ ( 1 ) - ( \bar ( X ) ) \vdots X_ ( n ) - ( \bar ( X ) ) Y_ ( 1 ) - ( \bar ( Y ) ) \vdots Y_ ( n ) - ( \bar ( Y ) ) Z_ ( 1 ) - ( \bar ( Z ) ) \vdots Z_ ( n ) - ( \bar ( Z ) ) end ( pmatrix ) ). ) The observation vector, on the left - hand side, has 3n degrees of freedom. On the right - hand side, the first vector has one degree of freedom ( or dimension ) for the overall mean. The second vector depends on three random variables, X  ̄ − M  ̄ ( displaystyle ( \bar ( X ) ) - ( \bar ( M ) ) ), Y  ̄ − M  ̄ ( displaystyle ( \bar ( Y ) ) - ( \bar ( M ) ) ) and Z  ̄ − M  ̄ ( displaystyle ( overline ( Z ) ) - ( overline ( M ) ) ). However, these must sum to 0 and so are constrained ; the vector therefore must lie in a 2 - dimensional subspace, and has 2 degrees of freedom. The remaining 3n − 3 degrees of freedom are in the residual vector ( made up of n − 1 degrees of freedom within each of the populations ). In analysis of variance ( ANOVA ) In statistical testing problems, one usually is n't interested in the component vectors themselves, but rather in their squared lengths, or Sum of Squares. The degrees of freedom associated with a sum - of - squares is the degrees - of - freedom of the corresponding component vectors. The three - population example above is an example of one - way Analysis of Variance. The model, or treatment, sum - of - squares is the squared length of the second vector, SSTr = n ( X  ̄ − M  ̄ ) 2 + n ( Y  ̄ − M  ̄ ) 2 + n ( Z  ̄ − M  ̄ ) 2 ( displaystyle ( \text ( SSTr ) ) = n ( ( \bar ( X ) ) - ( \bar ( M ) ) ) ^ ( 2 ) + n ( ( \bar ( Y ) ) - ( \bar ( M ) ) ) ^ ( 2 ) + n ( ( \bar ( Z ) ) - ( \bar ( M ) ) ) ^ ( 2 ) ) with 2 degrees of freedom. The residual, or error, sum - of - squares is SSE = ∑ i = 1 n ( X i − X  ̄ ) 2 + ∑ i = 1 n ( Y i − Y  ̄ ) 2 + ∑ i = 1 n ( Z i − Z  ̄ ) 2 ( displaystyle ( \text ( SSE ) ) = sum _ ( i = 1 ) ^ ( n ) ( X_ ( i ) - ( \bar ( X ) ) ) ^ ( 2 ) + sum _ ( i = 1 ) ^ ( n ) ( Y_ ( i ) - ( \bar ( Y ) ) ) ^ ( 2 ) + sum _ ( i = 1 ) ^ ( n ) ( Z_ ( i ) - ( \bar ( Z ) ) ) ^ ( 2 ) ) with 3 ( n − 1 ) degrees of freedom. Of course, introductory books on ANOVA usually state formulae without showing the vectors, but it is this underlying geometry that gives rise to SS formulae, and shows how to unambiguously determine the degrees of freedom in any given situation. Under the null hypothesis of no difference between population means ( and assuming that standard ANOVA regularity assumptions are satisfied ) the sums of squares have scaled chi - squared distributions, with the corresponding degrees of freedom. The F - test statistic is the ratio, after scaling by the degrees of freedom. If there is no difference between population means this ratio follows an F distribution with 2 and 3n − 3 degrees of freedom. In some complicated settings, such as unbalanced split - plot designs, the sums - of - squares no longer have scaled chi - squared distributions. Comparison of sum - of - squares with degrees - of - freedom is no longer meaningful, and software may report certain fractional ' degrees of freedom ' in these cases. Such numbers have no genuine degrees - of - freedom interpretation, but are simply providing an approximate chi - squared distribution for the corresponding sum - of - squares. The details of such approximations are beyond the scope of this page. In probability distributions Several commonly encountered statistical distributions ( Student 's t, Chi - Squared, F ) have parameters that are commonly referred to as degrees of freedom. This terminology simply reflects that in many applications where these distributions occur, the parameter corresponds to the degrees of freedom of an underlying random vector, as in the preceding ANOVA example. Another simple example is : if X i ; i = 1,..., n ( displaystyle X_ ( i ) ; i = 1, ldots, n ) are independent normal ( μ, σ 2 ) ( displaystyle ( mu, sigma ^ ( 2 ) ) ) random variables, the statistic ∑ i = 1 n ( X i − X  ̄ ) 2 σ 2 ( displaystyle ( \frac ( sum limits _ ( i = 1 ) ^ ( n ) ( X_ ( i ) - ( \bar ( X ) ) ) ^ ( 2 ) ) ( sigma ^ ( 2 ) ) ) ) follows a chi - squared distribution with n − 1 degrees of freedom. Here, the degrees of freedom arises from the residual sum - of - squares in the numerator, and in turn the n − 1 degrees of freedom of the underlying residual vector ( X i − X  ̄ ) ( displaystyle ( X_ ( i ) - ( \bar ( X ) ) ) ). In the application of these distributions to linear models, the degrees of freedom parameters can take only integer values. The underlying families of distributions allow fractional values for the degrees - of - freedom parameters, which can arise in more sophisticated uses. One set of examples is problems where chi - squared approximations based on effective degrees of freedom are used. In other applications, such as modelling heavy - tailed data, at or F distribution may be used as an empirical model. In these cases, there is no particular degrees of freedom interpretation to the distribution parameters, even though the terminology may continue to be used. In non-parametric regression Many non-standard regression methods, including ridge regression, linear smoothers, smoothing splines, and semiparametric regression are not based on ordinary least squares projections, but rather on regularized ( generalized and / or penalized ) least - squares, and so degrees of freedom defined in terms of dimensionality is generally not useful for these procedures. However, these procedures are still linear in the observations, and the fitted values of the regression can be expressed in the form y ^ = H y, ( displaystyle ( hat ( y ) ) = Hy, , ) where y ^ ( displaystyle ( hat ( y ) ) ) is the vector of fitted values at each of the original covariate values from the fitted model, y is the original vector of responses, and H is the hat matrix or, more generally, smoother matrix. For statistical inference, sums - of - squares can still be formed : the model sum - of - squares is ∥ H y ∥ 2 ( displaystyle Hy ^ ( 2 ) ) ; the residual sum - of - squares is ∥ y − H y ∥ 2 ( displaystyle y - Hy ^ ( 2 ) ). However, because H does not correspond to an ordinary least - squares fit ( i.e. is not an orthogonal projection ), these sums - of - squares no longer have ( scaled, non-central ) chi - squared distributions, and dimensionally defined degrees - of - freedom are not useful. The effective degrees of freedom of the fit can be defined in various ways to implement goodness - of - fit tests, cross-validation, and other statistical inference procedures. Here one can distinguish between regression effective degrees of freedom and residual effective degrees of freedom. Regression effective degrees of freedom For the regression effective degrees of freedom, appropriate definitions can include the trace of the hat matrix, tr ( H ), the trace of the quadratic form of the hat matrix, tr ( H'H ), the form tr ( 2H -- H H ' ), or the Satterthwaite approximation, tr ( H'H ) / tr ( H'HH'H ). In the case of linear regression, the hat matrix H is X ( X ' X ) X ', and all these definitions reduce to the usual degrees of freedom. Notice that tr ⁡ ( H ) = ∑ i h i i = ∑ i ∂ y ^ i ∂ y i, ( displaystyle operatorname ( tr ) ( H ) = sum _ ( i ) h_ ( ii ) = sum _ ( i ) ( \frac ( partial ( hat ( y ) ) _ ( i ) ) ( partial y_ ( i ) ) ), ) the regression ( not residual ) degrees of freedom in linear models are `` the sum of the sensitivities of the fitted values with respect to the observed response values '', i.e. the sum of leverage scores. One way to help to conceptualize this is to consider a simple smoothing matrix like a Gaussian blur function. The Gaussian blur is an attempt to estimate the values of a smoothly varying function from otherwise noisy data. In contrast to a simple linear or polynomial fit, computing the effective degrees of freedom of the smoothing function is not straight - forward. In these cases, it is important to estimate the Degrees of Freedom permitted by the H ( displaystyle H ) matrix so that the residual degrees of freedom can then be used to estimate statistical tests such as χ 2 ( displaystyle chi ^ ( 2 ) ). Residual effective degrees of freedom There are corresponding definitions of residual effective degrees - of - freedom ( redf ), with H replaced by I − H. For example, if the goal is to estimate error variance, the redf would be defined as tr ( ( I − H ) ' ( I − H ) ), and the unbiased estimate is ( with r ^ = y − H y ( displaystyle ( hat ( r ) ) = y - Hy ) ), σ ^ 2 = ∥ r ^ ∥ 2 tr ( ( I − H ) ′ ( I − H ) ), ( displaystyle ( hat ( sigma ) ) ^ ( 2 ) = ( \frac ( ( hat ( r ) ) ^ ( 2 ) ) ( ( hbox ( tr ) ) left ( ( I-H ) ' ( I-H ) \right ) ) ), ) or : σ ^ 2 = ∥ r ^ ∥ 2 n − tr ⁡ ( 2 H − H H ′ ) = ∥ r ^ ∥ 2 n − 2 tr ⁡ ( H ) + tr ⁡ ( H H ′ ) ( displaystyle ( hat ( sigma ) ) ^ ( 2 ) = ( \frac ( ( hat ( r ) ) ^ ( 2 ) ) ( n - operatorname ( tr ) ( 2H - HH ' ) ) ) = ( \frac ( ( hat ( r ) ) ^ ( 2 ) ) ( n - 2 operatorname ( tr ) ( H ) + operatorname ( tr ) ( HH ' ) ) ) ) σ ^ 2 ≈ ∥ r ^ ∥ 2 n − 1.25 tr ⁡ ( H ) + 0.5. ( displaystyle ( hat ( sigma ) ) ^ ( 2 ) approx ( \frac ( ( hat ( r ) ) ^ ( 2 ) ) ( n - 1.25 operatorname ( tr ) ( H ) + 0.5 ) ). ) The last approximation above reduces the computational cost from O ( n ) to only O ( n ). In general the numerator would be the objective function being minimized ; e.g., if the hat matrix includes an observation covariance matrix, Σ, then ∥ r ^ ∥ 2 ( displaystyle ( hat ( r ) ) ^ ( 2 ) ) becomes r ^ ′ Σ − 1 r ^ ( displaystyle ( hat ( r ) ) ' Sigma ^ ( - 1 ) ( hat ( r ) ) ). General Note that unlike in the original case, non-integer degrees of freedom are allowed, though the value must usually still be constrained between 0 and n. Consider, as an example, the k - nearest neighbour smoother, which is the average of the k nearest measured values to the given point. Then, at each of the n measured points, the weight of the original value on the linear combination that makes up the predicted value is just 1 / k. Thus, the trace of the hat matrix is n / k. Thus the smooth costs n / k effective degrees of freedom. As another example, consider the existence of nearly duplicated observations. Naive application of classical formula, n − p, would lead to over-estimation of the residuals degree of freedom, as if each observation were independent. More realistically, though, the hat matrix H = X ( X ' Σ X ) X ' Σ would involve an observation covariance matrix Σ indicating the non-zero correlation among observations. The more general formulation of effective degree of freedom would result in a more realistic estimate for, e.g., the error variance σ, which in its turn scales the unknown parameters ' a posteriori standard deviation ; the degree of freedom will also affect the expansion factor necessary to produce an error ellipse for a given confidence level. Other formulations Similar concepts are the equivalent degrees of freedom in non-parametric regression, the degree of freedom of signal in atmospheric studies, and the non-integer degree of freedom in geodesy. Alternative This section needs expansion. You can help by adding to it. ( August 2013 ) The residual sum - of - squares ∥ y − H y ∥ 2 ( displaystyle y - Hy ^ ( 2 ) ) has a generalized chi - squared distribution, and the theory associated with this distribution provides an alternative route to the answers provided above. See also Statistics portal Chi - squared per degree of freedom Pooled degrees of freedom Replication ( statistics ) Sample size Statistical model Variance References Jump up ^ `` Degrees of Freedom ''. `` Glossary of Statistical Terms ''. Animated Software. Retrieved 2008 - 08 - 21. Jump up ^ Lane, David M. `` Degrees of Freedom ''. HyperStat Online. Statistics Solutions. Retrieved 2008 - 08 - 21. Jump up ^ Walker, H.M. ( April 1940 ). `` Degrees of Freedom ''. Journal of Educational Psychology. 31 ( 4 ) : 253 -- 269. doi : 10.1037 / h0054588. Jump up ^ Walker, H.M. ( April 1940 ). `` Degrees of Freedom '' ( PDF ). Journal of Educational Psychology. 31 ( 4 ) : 253 -- 269. doi : 10.1037 / h0054588. Jump up ^ Student ( March 1908 ). `` The Probable Error of a Mean ''. Biometrika. 6 ( 1 ) : 1 -- 25. JSTOR 2331554. doi : 10.2307 / 2331554. Jump up ^ Fisher, R.A. ( January 1922 ). `` On the Interpretation of χ2 from Contingency Tables, and the Calculation of P ''. Journal of the Royal Statistical Society. 85 ( 1 ) : 87 -- 94. JSTOR 2340521. doi : 10.2307 / 2340521. Jump up ^ Christensen, Ronald ( 2002 ). Plane Answers to Complex Questions : The Theory of Linear Models ( Third ed. ). New York : Springer. ISBN 0 - 387 - 95361 - 2. Jump up ^ Trevor Hastie, Robert Tibshirani, Jerome H. Friedman ( 2009 ), The elements of statistical learning : data mining, inference, and prediction, 2nd ed., 746 p. ISBN 978 - 0 - 387 - 84857 - 0, doi : 10.1007 / 978 - 0 - 387 - 84858 - 7, ( 1 ) ( eq. ( 5.16 ) ) Jump up ^ Ye, J. ( 1998 ), `` On Measuring and Correcting the Effects of Data Mining and Model Selection '', Journal of the American Statistical Association, 93 ( 441 ), 120 -- 131. JSTOR 2669609 ( eq. ( 7 ) ) Jump up ^ Clive Loader ( 1999 ), Local regression and likelihood, ISBN 978 - 0 - 387 - 98775 - 0, doi : 10.1007 / b98858, ( eq. ( 2.18 ), p. 30 ) ^ Jump up to : Trevor Hastie, Robert Tibshirani ( 1990 ), Generalized additive models, CRC Press, ( p. 54 ) and ( eq. ( B. 1 ), p. 305 ) ) Jump up ^ Simon N. Wood ( 2006 ), Generalized additive models : an introduction with R, CRC Press, ( eq. ( 4, 14 ), p. 172 ) Jump up ^ David Ruppert, M.P. Wand, R.J. Carroll ( 2003 ), Semiparametric Regression, Cambridge University Press ( eq. ( 3.28 ), p. 82 ) Jump up ^ Peter J. Green, B.W. Silverman ( 1994 ), Nonparametric regression and generalized linear models : a roughness penalty approach, CRC Press ( eq. ( 3.15 ), p. 37 ) Jump up ^ Clive D. Rodgers ( 2000 ), Inverse methods for atmospheric sounding : theory and practice, World Scientific ( eq. ( 2.56 ), p. 31 ) Jump up ^ Adrian Doicu, Thomas Trautmann, Franz Schreier ( 2010 ), Numerical Regularization for Atmospheric Inverse Problems, Springer ( eq. ( 4.26 ), p. 114 ) Jump up ^ D. Dong, T.A. Herring and R.W. King ( 1997 ), Estimating regional deformation from a combination of space and terrestrial geodetic data, J. Geodesy, 72 ( 4 ), 200 -- 214, doi : 10.1007 / s001900050161 ( eq. ( 27 ), p. 205 ) Jump up ^ H. Theil ( 1963 ), `` On the Use of Incomplete Prior Information in Regression Analysis '', Journal of the American Statistical Association, 58 ( 302 ), 401 -- 414 JSTOR 2283275 ( eq. ( 5.19 ) -- ( 5.20 ) ) Jump up ^ Jones, D.A. ( 1983 ) `` Statistical analysis of empirical models fitted by optimisation '', Biometrika, 70 ( 1 ), 67 -- 88 Further reading Bowers, David ( 1982 ). Statistics for Economists. London : Macmillan. pp. 175 -- 178. ISBN 0 - 333 - 30110 - 2. Eisenhauer, J.G. ( 2008 ). `` Degrees of Freedom ''. Teaching Statistics. 30 ( 3 ) : 75 -- 78. doi : 10.1111 / j. 1467 - 9639.2008. 00324. x. Good, I.J. ( 1973 ). `` What Are Degrees of Freedom ? ''. The American Statistician. 27 ( 5 ) : 227 -- 228. JSTOR 3087407. doi : 10.1080 / 00031305.1973. 10479042. Walker, H.W. ( 1940 ). `` Degrees of Freedom ''. Journal of Educational Psychology. 31 ( 4 ) : 253 -- 269. doi : 10.1037 / h0054588. Transcription by C Olsen with errata External links Yu, Chong - ho ( 1997 ) Illustrating degrees of freedom in terms of sample size and dimensionality Dallal, GE. ( 2003 ) Degrees of Freedom Statistics Outline Index Descriptive statistics Continuous data Center Mean arithmetic geometric harmonic Median Mode Dispersion Variance Standard deviation Coefficient of variation Percentile Range Interquartile range Shape Central limit theorem Moments Skewness Kurtosis L - moments Count data Index of dispersion Summary tables Grouped data Frequency distribution Contingency table Dependence Pearson product - moment correlation Rank correlation Spearman 's rho Kendall 's tau Partial correlation Scatter plot Graphics Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q -- Q plot Run chart Scatter plot Stem - and - leaf display Radar chart Data collection Study design Population Statistic Effect size Statistical power Sample size determination Missing data Survey methodology Sampling stratified cluster Standard error Opinion poll Questionnaire Controlled experiments Design control optimal Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment Uncontrolled studies Observational study Natural experiment Quasi-experiment Statistical inference Statistical theory Population Statistic Probability distribution Sampling distribution Order statistic Empirical distribution Density estimation Statistical model L space Parameter location scale shape Parametric family Likelihood ( monotone ) Location -- scale family Exponential family Completeness Sufficiency Statistical functional Bootstrap U V Optimal decision loss function Efficiency Statistical distance divergence Asymptotics Robustness Frequentist inference Point estimation Estimating equations Maximum likelihood Method of moments M - estimator Minimum distance Unbiased estimators Mean - unbiased minimum - variance Rao -- Blackwellization Lehmann -- Scheffé theorem Median unbiased Plug - in Interval estimation Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife Testing hypotheses 1 - & 2 - tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons Parametric tests Likelihood - ratio Wald Score Specific tests Z ( normal ) Student 's t - test Goodness of fit Chi - squared Kolmogorov -- Smirnov Anderson -- Darling Lilliefors Jarque -- Bera Normality ( Shapiro -- Wilk ) Likelihood - ratio test Model selection Cross validation AIC BIC Rank statistics Sign Sample median Signed rank ( Wilcoxon ) Hodges -- Lehmann estimator Rank sum ( Mann -- Whitney ) Nonparametric anova 1 - way ( Kruskal -- Wallis ) 2 - way ( Friedman ) Ordered alternative ( Jonckheere -- Terpstra ) Bayesian inference Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator Correlation Regression analysis Correlation Pearson product - moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines ( MARS ) Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard predictors Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity Generalized linear model Exponential families Logistic ( Bernoulli ) / Binomial / Poisson regressions Partition of variance Analysis of variance ( ANOVA, anova ) Analysis of covariance Multivariate ANOVA Degrees of freedom Categorical / Multivariate / Time - series / Survival analysis Categorical Cohen 's kappa Contingency table Graphical model Log - linear model McNemar 's test Multivariate Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model Factor analysis Multivariate distributions Elliptical distributions Normal Time - series General Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality Specific tests Dickey -- Fuller Johansen Q - statistic ( Ljung -- Box ) Durbin -- Watson Breusch -- Godfrey Time domain Autocorrelation ( ACF ) partial ( PACF ) Cross-correlation ( XCF ) ARMA model ARIMA model ( Box -- Jenkins ) Autoregressive conditional heteroskedasticity ( ARCH ) Vector autoregression ( VAR ) Frequency domain Spectral density estimation Fourier analysis Wavelet Survival Survival function Kaplan -- Meier estimator ( product limit ) Proportional hazards models Accelerated failure time ( AFT ) model First hitting time Hazard function Nelson -- Aalen estimator Test Log - rank test Applications Biostatistics Bioinformatics Clinical trials / studies Epidemiology Medical statistics Engineering statistics Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification Social statistics Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population statistics Psychometrics Spatial statistics Cartography Environmental statistics Geographic information system Geostatistics Kriging Portal Commons WikiProject Retrieved from `` https://en.wikipedia.org/w/index.php?title=Degrees_of_freedom_(statistics)&oldid=806489068 '' Categories : Statistical theory Hidden categories : All articles with links needing disambiguation Articles with links needing disambiguation from September 2017 Articles to be expanded from August 2013 All articles to be expanded Articles using small message boxes Talk Contents Interaction About Wikipedia Català Deutsch Español Esperanto فارسی Français 한국어 Italiano 日本 語 Norsk Norsk nynorsk Polski Português Русский Slovenščina Српски / srpski Basa Sunda Suomi Svenska Türkçe 中文 Edit links This page was last edited on 22 October 2017, at 10 : 32. 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Question: what do u mean by degree of freedom
Answer: the number of values in the final calculation of a statistic that are free to vary