Analysis of molecularvariance AMOVA , is a statistical model for the molecular variation in a single species , typically biology biological . ref cite journal pmid 1644282 year 1992 month Jun author Excoffier, L Smouse, Pe Quattro, Jm title Analysis of molecularvariance inferred from metric distances among DNA haplotypes application to human mitochondrial DNA restriction data volume 131 issue 2 pages 479 91 issn 0016 6731 pmc 1205020 journal Genetics format Free full text ref The name and model are inspired by ANOVA . The method was developed by Laurent Excoffier , Peter Smouse and Joseph Quattro at Rutgers University in 1992. Since developing AMOVA, Excoffier has written a computer program program for running such analyses. This program, which runs on Microsoft Windows Windows is called Arlequin , and is freely available on Excoffier s website. There is also an implementation by Sandrine Pavoine in R language in the ade4 package available on CRAN Comprehensive R Archive Network . Another implementation is in Info Gen , which also runs on Microsoft Windows Windows . Student version if free and is fully functional. Native language of the application is Spanish but an English version is also available. External links http cmpg.unibe.ch software arlequin3 Arlequin 3 website http www.yhrd.org Analyse Online AMOVA Online AMOVA Calculation for Y STR Data http www.info gen.com.ar Info Gen website References references popgen br Category Population genetics Category Genetics Category Molecular biology Category Analysis of variance statistics stub ... more details
In statistics , analysis of variance ANOVA is a collection of statistical model s, and their associated procedures, in which the observed variance in a particular variable is partitioned into components ... in comparing two, three or more means. Models There are three classes of models used in the analysis of variance, and these are outlined here. Fixed effects models Model 1 Main Fixed effects model The fixed effects model of analysis of variance applies to situations in which the experimenter ... of both fixed and random effects types, with appropriately different interpretations and analysis for the two types. Assumptions of ANOVA The analysis of variance has been studied from several approaches ... model for which an analysis of variance may be appropriate. A model often presented in textbooks Many textbooks present the analysis of variance in terms of a linear model , which makes the following ... . Equality or homogeneity of variances, called homoscedasticity &mdash the variance of data in groups should be the same. Model based approaches usually assume that the variance is constant. The constant variance property also appears in the randomization design based analysis of randomized experiments ... treatment e.g., in a longitudinal study . Multivariate analysis of variance MANOVA is used when there is more than one dependent variable response variable . History The analysis of variance was used ... date November 2010 Sir Ronald Fisher proposed a formal analysis of variance in a 1918 article The Correlation ... Deduced from a Small Sample. Ronald A. Fisher. Metron, 1 3 32 1921 ref Analysis of variance became ... ANOVA simultaneous component analysis AMOVA ANCOVA ANORVA MANOVA Mixed design analysis of variance div ... 625. Lindman, H. R. 1974 . Analysis of variance in complex experimental designs . San Francisco W. H ... Of Variance Category Analysis of variance Category Design of experiments Category Statistical tests ... of cases &ndash this is an assumption of the model that simplifies the statistical analysis ... more details
In statistics , analysis of rhythmic variance ANORVA is a method for detecting rhythms in biological time series , published by Peter Celec Biol Res. 2004, 37 4 Suppl A 777 82 . It is a procedure for detecting cyclic variations in biological time series and quantification of their probability. ANORVA is based on the premise that the variance in groups of data from rhythmic variables is low when a time distance of one period exists between the data entries. External links http www.ncbi.nlm.nih.gov entrez query.fcgi?db pubmed&cmd Retrieve&dopt AbstractPlus&list uids 15586826&query hl 1&itool pubmed docsum Analysis of rhythmic variance ANORVA. A new simple method for detecting rhythms in biological time series. http www.scielo.cl scielo.php?pid S0716 97602004000500007&script sci arttext&tlng en Analysis of Rhythmic Variance statistics stub Category Analysis of variance Category Time series analysis ... more details
In statistics , a mixed design analysis of variance model also known as a split plot ANOVA is used to test for differences between two or more independent groups whilst subjecting participants to repeated measures . Thus, in a mixed design ANOVA model, one factor a fixed effects model fixed effects factor is a between subjects variable and the other a random effects model random effects factor is a within subjects variable. Thus, overall, the model is a type of Mixed model mixed effect model . See also Multilevel model Restricted randomization No footnotes date July 2010 wikiversity Mixed design ANOVA Category Analysis of variance statistics stub ... more details
Cleanup date April 2009 Multivariate analysis of variance MANOVA is a statistical test procedure for comparing multivariate population means of several groups. Unlike ANOVA, it uses the variance covariance between variables in testing the statistical significance of the mean differences. It is a generalized form of univariate analysis of variance ANOVA . It is used when there are two or more dependent variables. It helps to answer 1. do changes in the independent variable s have significant effects on the dependent variables 2. what are the interactions among the dependent variables and 3. among the independent variables. ref Stevens, J. P. 2002 . Applied multivariate statistics for the social sciences. Mahwah, NJ Lawrence Erblaum. ref Where sums of squares appear in univariate analysis of variance, in multivariate analysis of variance certain positive definite matrix positive definite matrices appear. The diagonal entries are the same kinds of sums of squares that appear in univariate ANOVA . The off diagonal entries are corresponding sums of products. Under normality assumptions about errors and residuals in statistics error distributions, the counterpart of the sum of squares due to error has a Wishart distribution . Analogous to ANOVA , MANOVA is based on the product of model variance matrix, math Sigma model math and inverse of the error variance matrix, math Sigma res 1 math , or math A Sigma model times Sigma res 1 math . The hypothesis that math Sigma model Sigma residual math implies that the product math A sim I math ref cite web last Carey first Gregory title Multivariate Analysis of Variance MANOVA I. Theory url http ibgwww.colorado.edu carey p7291dir handouts manova1.pdf accessdate 2011 03 22 ref . Invariance considerations imply the MANOVA statistic should ... Statistics Experimental design Category Analysis of variance Category Design of experiments es An lisis ... . References reflist See also Discriminant function analysis Repeated measures design External ... more details
Multiple issues lead missing May 2011 context May 2011 inappropriate person May 2011 Total Variance saving ... Cost Price per unit Planned Volume Cost Price Variance saving loss It shows if actual volume had ... to be marginal cost price Volume Variance saving loss It shows if planned unit cost price ... variance can be expresssed as br total variance cost price variance volume variance br P2 V2 P1 V1 P2 P1 V2 V2 V1 P1 Volume Variance can be further broken down into Product Mix Variance saving ... mix variance can only exist if there are more than one product in the plan and or actual. Product Yield Variance saving loss It shows if planned unit cost price and planned p mix is actualized, what ... 1 Va2 Actual Volume for product 2 then volume variance can be expressed as br volume variance product mix variance product yield variance br Va1 Vp1 Pp1 Va2 Vp2 Pp2 Va1 Vp1 Va1 Va2 Vp1 Vp2 Pp1 ... lbs. Let s first see how total variance, cost, and volume variances are calculated Total Variance ... Actual Variance Volume MM lbs Cost lb Cost MM Volume MM lbs Cost lb Cost MM Total Variance MM PRODUCT A 10 5 50 14 3 42 8 PRODUCT B 5 4 20 10 6 60 40 TOTAL 15 4.67 70 24 4.25 102 32 Total Variance 14 3 10 5 10 6 5 4 8 40 32MM Unfavorable or Loss Cost Variance class wikitable Basecase Plan Basecase Plan Basecase Plan Strategy Actual Strategy Actual Strategy Actual Variance Volume MM lbs Cost lb Cost MM Volume MM lbs Cost lb Cost MM Cost Variance MM PRODUCT A 14 5 70 14 3 42 28 PRODUCT B 10 4 40 10 6 60 20 TOTAL 24 4.58 110 24 4.25 102 8 Cost Variance 3 5 14 6 4 10 28 20 8MM Favorable or Saving Volume Variance class wikitable Basecase Plan Basecase Plan Basecase Plan Strategy Actual Strategy Actual Strategy Actual Variance Volume MM lbs Cost lb Cost MM Volume MM lbs Cost lb Cost MM Volume Variance MM PRODUCT A 10 5 50 14 5 70 20 PRODUCT B 15 4 20 10 4 40 20 TOTAL 15 4.67 70 24 4.58 110 40 Volume Variance 14 10 5 10 5 4 20 20 40MM Unfavorable or Loss Volume Variance can be further broken ... more details
In statistics, the two way analysis of variance ANOVA test is an extension to the one way ANOVA test that attempts to examine the influence of different categorical independent variables on one dependent variable. While the one way ANOVA measures the significant effects of one independent variable IV , the two way ANOVA is used when there are more than one level of IVs and multiple observations at each IV level. The two way ANOVA can not only determine the main effect of contributions of each independent variable level but also identify if the interaction effect among these groups exist. Assumptions to use two way anova Just like other parametric test, we make the following assumptions when using two way ANOVA The populations from which the samples are obtained must be normally distributed. Sampling is done correctly. Observations for within and between groups must be independent. The variances among populations must be equal homogeneity . Data are interval or nominal. Sample problem File Two way Anova data.png thumb 900px alt alt text center With two way ANOVA test, we can test the main effect at each IV level. This part of the procedure is pretty close to one way ANOVA test. However, in addition to the main effect, the interaction effect across different levels of IVs is also evaluated. And in order to do so, we set up the null and alternative hypothesis like other statistics tests. The null hypothesis states that the means of observations grouped by one factor are the same that the means of observations grouped by the other factor are the same and that there is no interaction between the two factors. On the other hand, the alternative hypothesis indicates there is at least one statistically significant difference among the groups. In the example illustrated below, the experiment ... and or analysis of variance, the difference between a set of scores, and is commonly used by most psychologists ... guides.html Category Analysis of variance ... more details
In statistics , the Kruskal Wallis one way analysis of variance by ranks named after William Kruskal and W. Allen Wallis is a non parametric statistics non parametric method for testing whether samples originate from the same distribution. It is used for comparing more than two samples that are independent, or not related. The parametric equivalence of the Kruskal Wallis test is the one way analysis of variance ANOVA . The factual null hypothesis is that the populations from which the samples originate, have the same median . When the Kruskal Wallis test leads to significant results, then at least one of the samples is different from the other samples. The test does not identify where the differences occur or how many differences actually occur. It is an extension of the Mann Whitney U test to 3 or more groups.The Mann Whitney would help analyze the specific sample pairs for significant differences. Since it is a non parametric method, the Kruskal Wallis test does not assume a normal distribution normal population, unlike the analogous one way analysis of variance . However, the test does assume an identically shaped and scaled distribution for each group, except for any difference in median s. Method Rank all data from all groups together i.e., rank the data from 1 to N ignoring group membership. Assign any tied values the average of the ranks they would have received had they not been tied. The test statistic is given by math K N 1 frac sum i 1 g n i bar r i cdot bar r 2 sum i 1 g sum j 1 n i r ij bar r 2 , math where math n i math is the number of observations in group math i math math r ij math is the rank among all observations of observation math j math from group math i math math N math is the total number of observations across all groups math bar r i cdot frac sum .... Use of ranks in one criterion varianceanalysis. Journal of the American Statistical Association ... Statistical tests Category Analysis of variance Category Non parametric statistics de Kruskal Wallis ... more details
MEGA , Molecular Evolutionary Genetics Analysis, is a freely available software to aid scientists and students in making dendrogram s, or phylogenetic tree s using nucleotide or protein sequences. It is developed by Koichiro Tamura from Tokyo Metropolitan University , Daniel Peterson , Nicholas Peterson , Glen Stecher , Sudhir Kumar from Arizona State University , and Masatoshi Nei from Pennsylvania State University . The manuscripts describing this resource are among the most highly cited in biology. ref name pmid8019868 cite journal author Kumar S, Tamura K, Nei M title MEGA Molecular Evolutionary Genetics Analysis software for microcomputers journal Comput. Appl. Biosci. volume 10 issue 2 pages 189 91 year 1994 month April pmid 8019868 doi url ref ref name pmid11751241 cite journal author Kumar S, Tamura K, Jakobsen IB, Nei M title MEGA2 molecular evolutionary genetics analysis software journal Bioinformatics volume 17 issue 12 pages 1244 5 year 2001 month December pmid 11751241 doi 10.1093 bioinformatics 17.12.1244 url ref ref name pmid15260895 cite journal author Kumar S, Tamura K, Nei M title MEGA3 Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment journal Brief. Bioinformatics volume 5 issue 2 pages 150 63 year 2004 month June pmid 15260895 doi url ref ref name pmid17488738 cite journal author Tamura K, Dudley J, Nei M, Kumar S title MEGA4 Molecular Evolutionary Genetics Analysis MEGA software version 4.0 journal Mol. Biol. Evol. volume 24 issue 8 pages 1596 9 year 2007 month August pmid 17488738 doi 10.1093 molbev msm092 url ref ref ... MEGA5 Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance ... analysis of DNA and protein sequences. 2008 Kumar S, Dudley J, Nei M & Tamura K. Briefings in Bioinformatics ... Category Data visualization software Category Bioinformatics software pt MEGA, Molecular Evolutionary Genetics Analysis ... more details
or the law of total variance and plays an important role in the analysis of variance . For example ... is often applied in analysis of variance , where the corresponding formula is math MS mbox Total MS ... anova.pdf A tutorial on Analysis of Variance devised for first year Oxford University students Theory ...About the mathematical concept other uses Variance disambiguation In probability theory and statistics , the variance is a measure of how far a set of numbers is spread out. It is one of several descriptors ... . In particular, the variance is one of the Moment mathematics moments of a distribution. In that context ... in terms of mathematical and computational simplicity. The variance is a population parameter ... of its variance in the simplest cases this estimate can be the sample variance , defined below. Basic discussion Examples The variance of a random variable or probability distribution distribution is the Expected ... from its expected value or mean. Thus the variance is a measure of the amount of variation of the values ... 16 2.5 1.5 0.5 0.5 1.5 2.5 1.5. math But its expected squared deviation its variance the mean of the equally ... 0 0.5 1 0.25 2 1, and the variance is 0.25 0 1 sup 2 sup 0.5 ... 0 0.25 0.5. Units of measurement Unlike expected absolute deviation, the variance ..., a variable measured in inches will have a variance measured in square inches. For this reason, describing ... using the variance. In the dice example the standard deviation is 2.9 1.7, slightly larger ... is more amenable to algebraic manipulation than the expected absolute deviation, and, together with variance ... . Estimating the variance Real world distributions such as the distribution of yesterday s rain .... Instead one Estimation theory estimates the mean and variance of the whole distribution as the computed mean and variance of a Sample statistics sample of n observations drawn suitably randomly from ... the variance by a factor of n 1 n . For example, when n 1 the variance of a single ... more details
variance am1 1 am2 2 am3 3 va1 F VarianceAnalysisVarianceanalysis , in budgeting or management .... Varianceanalysis can be carried out for both costs and revenues. See also Budgeting Non profit ...Unreferenced date December 2009 In budget ing or management accounting in general , a variance is the difference between a budgeted, planned or standard amount and the actual amount incurred sold. Variances can be computed for both costs and revenues. The concept of variance is intrinsically connected with planned and actual results and effects of the difference between those two on the performance of the entity or company. Types of variances Variances can be divided according to their effect or nature of the underlying amounts. When effect of variance is concerned, there are two types of variances When actual results are better than expected results given variance is described as favorable variance. In common use favorable variance is denoted by the letter F usually in parentheses F . When actual results are worse than expected results given variance is described as adverse variance, or unfavourable variance. In common use adverse variance is denoted by the letter A or the letter U usually in parentheses A . The second typology according to the nature of the underlying amount is determined by the needs of users of the variance information and may include e.g. Variable cost variances Direct material variance s Direct labour variance s Variable production overhead variances Fixed production overhead variances Sales variance s Example Let us assume that standard direct material cost of production is as follows 2 kg of Kevlar at &euro 60 per kg &euro 120 per unit of fuselage unit Let us assume further that during the given period, 200 units of fuselage were manufactured, using X kg of Kevlar which cost Y Direct material total variance can be calculated as Infobox VarianceAnalysis ... total variance direct material price variance direct material usage variance DEFAULTSORT Variance ... more details
Variance may refer to VarianceVariance statistics Variance accounting Variance land use Variance computer science Variance album Variance hungarian gamer community Disambiguation ... more details
Cosmic Variance may refer to Cosmic variance , a concept in cosmology Cosmic Variance blog disambig Long comment to avoid being listed on short pages ... more details
also referring to Cohen s d on page 6 DEFAULTSORT Pooled Variance Category Statistical terminology Category Analysis of variance ... is held constant. If, in order to achieve a small variance in y, numerous repeated tests are required at each value of x, the expense of testing may become prohibitive. Reasonable estimates of variance can be determined by using the principle of pooled variance after repeating each Statistical hypothesis testing test at a particular x only a few times. Pooled variance is a method for estimation theory estimating variance given several different sample statistics samples taken in different circumstances where the mean may vary between samples but the true variance equivalently, accuracy and precision ... n k 1 s k 2 n 1 n 2 cdots n k k math where s sub p sub sup 2 sup is the pooled variance, n sub i sub is the sample size of the i th sample, s sub i sub sup 2 sup is the variance of the i th sample, and k ... samples i.e. Bessel s correction . The square root of a pooled variance estimator is known as a pooled ... population variance. The latter one can give a more efficient math s p 2 math to estimate math sigma ..., variance and standard deviation are presented in the next table. border 1 cellspacing 0 cellpadding ... 3 2 29.5 4.5 2.12 4 5 20.6 4.3 2.07 5 5 19.0 2.5 1.58 These statistics represent the variance and standard ... a single estimate of variance and standard deviation. In a sense, this suggests finding a mean variance or standard deviation among the five results above. This mean variance is calculated by weighting the individual values with the size of the subset for each level of x . Thus, the pooled variance ... are their respective variances. The pooled variance of the data shown above is therefore math S P ... more details
Handbook of Frequency Stability Analysis ref The Allan variance is unable to distinguish between ... Allan variance . The confidence interval and degrees of freedom analysis, along with the established ...The Allan variance AVAR , also known as two sample variance , is a measure of frequency stability in clock ... as math sigma y 2 tau . , math The Allan deviation ADEV is the square root of Allan variance. It is also ... variance is a measure of frequency stability using M samples, time T between measures and observation time math tau math . M sample is expressed as math sigma y 2 M, T, tau . , math The Allan variance ... such as frequency drift or temperature effects. The Allan variance and Allan deviation ... variance Interpretation of value Interpretation of value below. There are also different adaptations or alterations of Allan variance . Notably the modified Allan variance MAVAR or MVAR, the total variance , and the Hadamard variance . There also exist time stability variants such as time deviation TDEV or time deviation time variance TVAR. Allan variance and its variants have proved useful outside ... processes are not unconditionally stable, but a derivative will be. The M sample variance is of historic ... of 2 sample variance with math T tau math now being called Allan variance . It remains important since it allows dead time in measurements and bias functions allows conversion into Allan variance values ... to be divergent. Efforts in analysing the stability provided both the theoretical analysis ref name ... the need for that application. To address these problems, David Allan introduced the M sample variance and indirectly the two sample variance. ref name Allan1966 While the two sample variance did not completely ... any M sample variance to any N sample variance via the common 2 sample variance, thus making all M sample variances comparable. The conversion mechanism also proved that M sample variance does not converge for large M, thus making them less useful. IEEE later identified the 2 sample variance ... more details
for the weblog Cosmic Variance blog cosmology Cosmic variance is the Statistics statistical uncertainty inherent in observations of the universe at extreme distances. It is based on the idea that it is only possible to observe part of the universe at one particular time, so it is difficult to make statistical statements about physical cosmology cosmology on the scale of the entire universe, ref name aspj Cite journal url http www.iop.org EJ article 1538 4357 600 2 L171 17416.html author Somerville et al. title Cosmic Variance in the Great Observatories Origins Deep Survey journal The Astrophysical Journal Letters year 2004 volume 600 issue 2 pages L171 L174 doi 10.1086 378628 last2 Lee first2 Kyoungsoo last3 Ferguson first3 Henry C. last4 Gardner first4 Jonathan P. last5 Moustakas first5 Leonidas A. last6 Giavalisco first6 Mauro arxiv astro ph 0309071 bibcode 2004ApJ...600L.171S ref ref name aas Cite web url http www.aas.org publications baas v37n4 aas207 1366.htm title Quantifying the Effects of Cosmic Variance Using the NOAO Deep Wide Field Survey accessdate September 18, 2007 publisher American Astronomical Society year 2006 author M.S. Keremedjiev Cornell University work 37 4 ref as the number of observations sample size must be too small. Background The standard big bang model ... about the model, unless the observer is careful to include the variance . This variance is called the cosmic variance and is separate from other sources of experimental error a very accurate measurement ... uncertainty about the underlying model. Variance is normally plotted separately from other sources ... , is to explicitly include the variance of very small statistical samples Poisson distribution when calculating uncertainty uncertainties . ref name oxford Cite journal title Analysis of the Kamionkowski Loeb method of reducing cosmic variance with CMB polarization publisher Department of Astrophysics ... Cosmic Variance Category Physical cosmology Category Statistical deviation and dispersion ... more details
Realized Variance RV is the sum of squared returns. For instance the RV can be the sum of squared daily returns for a particular month, which would yield a measure of price variation over this month. More commonly, the realized variance is computed as the sum of squared intraday returns for a particular day. The realized variance is useful because it provides a relatively accurate measure of volatility ref cite journal last Andersen first Torben G. last2 Bollerslev first2 Tim authorlink2 Tim Bollerslev year 1998 month title Answering the sceptics yes standard volatility models do provide accurate forecasts pages 885 905 journal International Economic Review volume 39 ref which is useful for many purposes, including volatility forecasting and forecast evaluation. Related quantities Unlike the variance the realized variance is a random quantity. The realized volatility is the square root of the realized variance, or the square root of the RV multiplied by a suitable constant to bring the measure of volatility to an annualized scale. For instance, if the RV is computed as the sum of squared daily returns for some month, then an annualized realized volatility is given by math sqrt 12 times RV math . Properties under ideal conditions Under ideal circumstances the RV consistently estimates the quadratic variation of the price process that the returns are computed from. ref cite journal last Barndorff Nielsen first Ole E. last2 Shephard first2 Neil authorlink Ole Barndorff Nielsen authorlink2 Neil Shephard year 2002 month May title Econometric analysis of realised volatility and its ... is some possibly random process for which the integrated variance, math IV int 0 t sigma s 2 ds, math is well defined. The realized variance based on math n math intraday returns is given by math RV ... variance converges to IV in probability. Moreover, the RV also converges in distribution in the sense ... year 2006 month April title Realized variance and market microstructure noise pages 127 218 doi 10.1198 ... more details
context date March 2011 Variance decomposition or forecast error variance decomposition indicates the amount of information each variable contributes to the other variables in a vector autoregression VAR models. ref L tkepohl, H, New Introduction to Multiple Time Series Analysis , Springer, 2007, p. 63. ref Variance decomposition determines how much of the forecast error variance of each of the variable can be explained by exogenous shocks to the other variables. Calculating the forecast error variance For the VAR p of form math y t nu A 1y t 1 dots A p y t p u math . Change this to a VAR 1 by writing it in companion form see general matrix notation of a VAR p math Y t mathbf nu A Y t 1 U math where math A begin bmatrix A 1 & A 2 & dots & A p 1 & A p mathbf I k & 0 & dots & 0 & 0 0 & mathbf I k & & 0 & 0 vdots & & ddots & vdots & vdots 0 & 0 & dots & mathbf I k & 0 end bmatrix math , math Y begin bmatrix y 1 vdots y p end bmatrix math , math mathbf nu begin bmatrix nu 0 vdots 0 end bmatrix math and math U begin bmatrix u 0 vdots 0 end bmatrix math where math y t math , math nu math and math u math are math k math dimensional column vectors, math A math is math kp math by math kp math dimensional matrix and math Y math , math mathbf nu math and math U math are math kp math dimensional column vectors. Calculate the mean squared error of the h step forecast of variable j, math mathbf MSE y j,t h math , math mathbf MSE y j,t h sum i 0 h 1 sum k 1 K e j Theta ie k 2 bigg sum i 0 h 1 Theta i Theta i bigg jj bigg sum i 0 h 1 Phi i Sigma u Phi i bigg jj , math where math e j math is the j sup th sup column of math I K math and the subscript math jj math refers to that element of the matrix. math Theta i Phi i P math where math P math is a lower triangular matrix obtained by a Cholesky decomposition of math Sigma u math such that math Sigma u PP math . math Phi i J A i J math ... u math . The amount of forecast error variance of variable math j math accounted for by exogenous shocks ... more details
In probability theory and statistics , a conditional variance is the variance of a conditional probability distribution . Particularly in econometrics , the conditional variance is also known as the scedastic function or skedastic function . Conditional variances are important parts of ARCH models. Definition The conditional variance of a random variable Y given that the value of a random variable X takes the value x is math operatorname Var Y X x operatorname E Y operatorname E Y mid X x 2 mid X x , math where E is the expectation operator with respect to the conditional distribution of Y given that the X takes the value x . An alternative notation for this is math operatorname Var Y mid X Y x . math The above may be stated in the alternative form that, based on the conditional distribution of Y given that the X takes the value x , the conditional variance is the variance of this probability distribution . Components of variance The law of total variance says math operatorname Var Y operatorname E operatorname Var Y mid X operatorname Var operatorname E Y mid X , math where, for example, math operatorname Var Y X math is understood to mean that the value x at which the conditional variance would is evaluated is allowed to be a random variable , X . In this law , the inner expectation or variance is taken with respect to Y conditional on X , while the outer expectation or variance is taken with respect to X . This expression represents the overall variance of Y as the sum of two components, involving a prediction of Y based on X . Specifically, let the predictor be the least mean squares prediction based on X , which is the conditional expectation of Y given X . Then the two components are the average of the variance of Y about the prediction based on X , as X varies the variance of the prediction based on X , as X varies. Category Statistical deviation and dispersion Category Statistical terminology Category Theory of probability distributions statistics stub probability ... more details
unreferenced date July 2007 Sales variance is the difference between actual sales and budget sales. It is used to measure the performance of a sales function, and or analyze business results to better understand market conditions. There are two reasons actual sales can vary from planned sales either the volume sold varied from plan sales volume variance , or sales were at a different price from what was planned sales price variance . Both scenarios could also simultaneously contribute to the variance. For example The plan was to sell 5 widgets at 3 each, for a budgeted sales of 5 3 15. In reality, 6 widgets were sold at 2 each, for an actual sales of 6 2 12. The total variance was thus 12 15 3 U nfavourable or minus 3, since total sales was less than planned. Sales price variance Sales Price Variance The sales price variance reveals the difference in total revenue caused by charging a different selling price from the planned or standard price. The sales price variance is calculated as Actual quantity sold actual selling price planned selling price . In the example, the sales price variance was 6 2 3 6 U nfavourable or minus 6, since the sales price was less than planned. Sales volume variance Sales Volume Variance is calculated as Budgeted price actual volume planned volume . In the example, the sales volume variance was 3 6 5 3 F avourable, or plus 3, since the sales volume was more than planned. Sales Volume Variance is further sub divided into two variances. Sales Mix Variance Sales Quantity Variance Total variance The total variance can thus be seen algebraic ally to be minus 6 plus plus 3 , giving minus 3 . Or 6 3 3. This result tells us that the negati ve effect of selling at a lower price was twice the positive effect of selling at a higher volume than planned. This might have occurred where prices were lowered to increase volume, but actual volume increases did not meet expectations, perhaps due to competitors also cutting their prices, or changes in customer ... more details
A variance swap is an over the counter finance over the counter financial derivative that allows one ... index . One leg of the swap will pay an amount based upon the realised variance of the price changes ... The features of a variance swap include the variance strike the realized variance the vega notional ... exchanged. However, in the case of a variance swap, the notional amount is specified in terms of The Greeks Vega .CE.BD vega , to convert the payoff into dollar terms. The payoff of a variance swap ... var math variance notional a.k.a. variance units , math sigma text realised 2 math annualised realised variance, and math sigma text strike 2 math variance strike. ref name FinCAD cite web last first authorlink coauthors title Variance and Volatility Swaps work publisher FinancialCAD Corporation ... 29 ref The annualised realised variance is calculated based on a prespecified set of sampling points over the period. It does not always coincide with the classic statistical definition of variance as the contract ... the realised variance. If this is done, it is common to use math n 1 math as the divisor rather than math n math , corresponding to an unbiased estimator estimate of the sample variance. It is market ... swap . ref name FinCAD cite web last first authorlink coauthors title Variance and Volatility Swaps ... VarianceSwaps.htm doi accessdate 2008 09 29 ref This makes the payoff of a variance swap comparable ... The variance swap may be hedged and hence priced using a portfolio of European call options ... Need To Know About Variance Swaps work publisher JPMorgan Equity Derivatives report year 2005 url http ... prices vanilla option vanilla options can therefore be used to price the variance swap. For example, using the Heston model , a closed form solution can be derived for the fair variance swap rate. Care ... effect on the price. We can derive the payoff of a variance swap using Ito s Lemma . We first assume ... dS t S t d log S t frac sigma 2 2 dt math Taking integrals, the total variance is math text Variance ... more details
Multiple issues unreferenced November 2007 context September 2009 The price variance Vmp of a material is computed as follows Vmp Actual unit cost Standard unit cost Actual Quantity Purchased or Vmp Actual Quantity Purchased Actual Unit Cost Actual Quantity Purchased Standard Unit Cost . When the Actual Materials Price is higher than the Standard Materials Price, the variance is said to be unfavorable, since the Actual price paid on materials purchased is greater than the allowed standard. The variance is said to be favorable when the Standard materials Price is higher than the Actual Materials Price, since less money was spent in purchasing the materials than the allowed standard. Category Economics terminology economics stub ... more details
Infobox Album See Wikipedia WikiProject Albums Name Variance Type Studio Artist Jega Cover Jega Variance.jpg Released 20 July 2009 Recorded Genre Electronic music Electronic Length 38 01 small Vol 1 small br 37 31 small Vol 2 small br 75 32 small total small Label Planet Mu Producer Jega Dylan J Nathan Reviews http reviews.headphonecommute.com 2010 02 13 jega variance planet mu Headphone Commute Last album Geometry Jega album Geometry br 2000 This album Variance br 2009 Next album Variance is the third album by electronic music ian Jega , released on 20 July 2009 by Planet Mu . ref cite web url http www.planet mu.com discography ZIQ024 title Planet Mu Records Discography Variance publisher Planet Mu accessdate 7 July 2009 ref Track listing tracklist headline Vol 1 title1 SoulFlute length1 4 41 title2 Antiphon length2 4 43 title3 Moment length3 3 07 title4 The Girl Who Fell to Earth length4 4 44 title5 Sakura length5 4 42 title6 Eva length6 4 27 title7 Dreams length7 4 36 title8 Aqueminae length8 4 01 title9 Zenith length9 3 00 tracklist headline Vol 2 title1 Tensor length1 0 32 title2 Shibuya length2 4 02 title3 Chromadynamic length3 4 44 title4 Cascade Decoherence length4 3 39 title5 Aerodynamic length5 4 36 title6 Latinhypercube length6 4 59 title7 Kyoto length7 5 22 title8 Hydrodynamic length8 4 23 title9 Reprise length9 5 14 References Reflist External links http www.planet mu.com discography ZIQ024 Variance at the Planet Mu website 2000s electronic album stub Category 2009 albums Category Jega albums Category Planet Mu albums Category Double albums ... more details
In mathematics, more specifically in the theory of Monte Carlo method s, variance reduction is a procedure used to increase the precision of the estimates that can be obtained for a given number of iterations. Every output random variable from the simulation is associated with a variance which limits the precision of the simulation results. In order to make a simulation statistically efficient, i.e., to obtain a greater precision and smaller confidence intervals for the output random variable of interest, variance reduction techniques can be used. The main ones are Common random numbers, antithetic variates , control variate s, importance sampling and stratified sampling . Under these headings are a variety of specialized techniques for example particle transport simulations make extensive use of weight windows and splitting Russian roulette techniques, which is a form of importance sampling. Common Random Numbers CRN The common random numbers variance reduction technique is a popular and useful variance reduction technique which applies when we are comparing two or more alternative configurations of a system instead of investigating a single configuration. CRN has also been called Correlated sampling , Matched streams or Matched pairs . CRN requires synchronization of the random number streams, which ensures that in addition to using the same random numbers to simulate all configurations, a specific random number used for a specific purpose in one configuration is used for exactly the same purpose in all other configurations. For example, in queueing theory, if we are comparing two different configurations of tellers in a bank, we would want the random time of arrival of the N ... that the variance is reduced. It can also be observed that if the CRN induces a negative correlation, i.e., Cov X sub 1 j sub , X sub 2 j sub 0, this technique can actually backfire, where the variance ... LA UR 03 1987 Category Monte Carlo methods Category Variance reduction fr R duction de la variance ... more details
Unreferenced date December 2006 Essay date April 2009 Time variance is the ability to remember historic perspectives. The requirement is to be able to know how something was classified or who owned something and how this changed as time passed. For the context of time and frequency and qualification of oscillators and amplifiers the technical terms time deviation and time deviation time variance is defined. Understanding time variance Future change, be it organizational, regulatory or geographical is hard to conceive. In 1988, who imagined that within a few years, Yugoslavia and East Germany would cease to exist? Enabling a data warehouse to report pre and post change information together in a meaningful context is very expensive and time consuming. Couple that with the pressure to rapidly meet other business requirements, and with the inability for any of us to predict change especially at system design time , and you can see why the time variant reporting requirement is often ignored. But at what expense? The real life case below illustrates the value of time variant reporting A beverage company paid rebates to customers at year end, based on ownership of customer sites at year end. The sales data warehouse did not reflect customers selling sites to one another throughout the year, resulting in mis payments and a multi million dollar customer service dilemma. For regulatory compliance and other reasons, data warehouses must remember how things were in the past because at some point business people will expect to be able to be report on them that way. DEFAULTSORT Time Variance Category Data warehousing Link GA de de Temporale Datenhaltung ... more details
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