
Review of Short Phrases and Links 
This Review contains major "Ancova" related terms, short phrases and links grouped together in the form of Encyclopedia article.
Definitions
 ANCOVA is the preferred method of analyzing randomized trials with baseline and posttreatment measures.
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 ANCOVA is used to increase power in a oneway or twoway ANOVA by adding a second or third variable as a covariate.
 ANCOVA is a merger of ANOVA and regression for continuous variables.
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 ANCOVA was generally superior to MannWhitney in most situations, especially where logtransformed data were entered into the model.
 ANCOVA is a linear model with both factors and continuous variables on the righthand side of the model formula.
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 ANCOVA is a statistical technique of controlling extraneous variables in correlational studies.
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Ancova
 Two null hypotheses are tested in an ancova.
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 ANCOVA is then used as the statistical technique to eliminate irrelevant y variance.
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 Using ancova is a statistical way of making it as if all the mussels were the same size, then comparing the mean AAM length of the averagesized mussel.
 Earlier editions are fine for ANCOVA and MANOVA.
 Relative quantities of mRNA from 16 different genes were evaluated using a statistical approach based on ANCOVA analysis.
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 ANCOVA is highly sensitive to outliers in the covariates.
 ANCOVA is sensitive to multicollinearity among the covariates and also loses statistical power as redundant covariates are added to the model.
 If there are covariates, ANCOVA is used instead of ANOVA. Covariates are commonly used as control variables.
 Also, ANCOVA should be used for interval level covariates.
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 Remember that in ANCOVA, we in essence perform a regression analysis within each cell to partition out the variance component due to the covariates.
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 If the covariate is related to the dependent in a linear manner, ANCOVA will be more powerful than ANOVA with blocking and is preferred.
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 ANCOVA provides a mechanism for assessing the differences in dependent variable scores after statistically controlling for the covariate.
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 The first step of ANCOVA is to regress the dependent variable on the covariate, ignoring group membership.
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 ANCOVA allows you to remove from a dependent variable ( y) irrelevant or error variance that can not be predicted from your independent variable ( x).
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 This is ANCOVA's "equality of regressions" or "homogeneity of regressions" assumption.
 Because all of these significance levels are greater than .05, the homogeneity of regression assumption has been met and you can proceed with the ANCOVA.
 Alternatives to traditional ANCOVA are also presented when circumstances in which certain assumptions have not been met.
 ANCOVA can be used in all ANOVA designs and the same assumptions apply.
 In ANCOVA, we was the effect of treatments as in ANOVA where the role of "covariates" is removed.
 Covariate effects: In ANCOVA, covariates are intervallevel independents.
 That is, in ANCOVA we look at the effects of the categorical independents on an interval dependent, after effects of interval covariates are controlled.
 A corresponding ANOVA table is constructed in which the columns are the various covariate (in ANCOVA), main, and interaction effects.
 The estimate of the treatment effect from ANCOVA was not importantly biased.
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 Mixed Model ANOVA and ANCOVA. Designs which contain random effects for one or more categorical predictor variables are called mixedmodel designs.
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 In general, between designs which contain both categorical and continuous predictor variables can be called ANCOVA designs.
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 If instead all 3 of these regression coefficients are zero the traditional ANCOVA design should be used.
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 Do you use the same designs (between groups, repeated measures, etc.) with ANCOVA as you do with ANOVA? By and large, yes.
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 When I understand ANCOVA and main effect better, I'll make separate entries for them.
 The text then goes on to cover the main designs, both independent and related ANOVA and ANCOVA, single and multifactor designs.
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 Univariate GLM is the version of the general linear model now often used to implement two longestablished statistical procedures  ANOVA and ANCOVA.
 ANCOVA, or an alysis of cova riance is a general linear model with one continuous explanatory variable and one or more factors.
 ANCOVA, or an alysis of cova riance is a general linear model with one continuous explanatory variable and one or more factors.
 Designs. ANOVA and ANCOVA have a number of different experimental designs.
 However, ANCOVA uses the pooled correlation between y and z for all experimental groups in the design.
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 ANOVA and ANCOVA are well suited to gauging interaction, whereas regression models can only do so my adding explicit crossproduct interaction terms.
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 If the slopes are not significantly different, the next step in an ancova is to draw a regression line through each group of points, all with the same slope.
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 The final test in the ancova is to test the null hypothesis that all of the Yintercepts of the regression lines with a common slope are the same.
 Remember that in ANCOVA, we in essence perform a regression analysis within each cell to partition out the variance component due to the covariates.
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 Regression Slope: ANCOVA, remember, operates on the pooled withingroup regression line of y on z.
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 However, in GLM ANCOVA, the values of the factors are adjusted for interactions with the covariates.
 The adjusted means of cDNA levels for 27 individuals were determined for all 14 genes adjusted with ACTB and GAPD in ANCOVA analysis.
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 Do you use the same designs (between groups, repeated measures, etc.) with ANCOVA as you do with ANOVA? By and large, yes.
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 The resulting ANCOVA will verify whether there are any differences in the posttest scores of the two groups after controlling for differences in ability.
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 As a precursor to the ANCOVA, let us perform a betweengroups t test to examine overall differences between the two groups on the final exam.
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 Analysis of covariance (ANCOVA) is a blending of regression and analysis of variance (Roscoe, 1975).
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 This model is called an analysis of covariance (ANCOVA) when one predictor variable is numeric (height) and the other is nominal (sex).
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 It is necessary to balance the effect of interaction in Analysis of covariance (ANCOVA) in order to avoid uncertainty among the independent variables.
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 Analysis of covariance (ANCOVA) is a blending of regression and analysis of variance (Roscoe, 1975).
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 The addition of a continuous variable to an existing ANOVA model is referred to as analysis of covariance (ANCOVA).
 The group means compared in ANCOVA are adjusted with the group means for the covariables (Gaddis 1998, Lee 1987, Statsoft 1999).
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 To determine differences in expression between sexes, time postmortem, and individuals, the software Statistica from Statsoft was used for ANCOVA analysis.
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 ANCOVA tests whether certain factors have an effect after controlling for quantitative predictors.
 ANCOVA tests whether certain factors have an effect after removing the variance for which quantitative predictors (covariates) account.
 As a second precursor to the ANCOVA, let us determine the degree of correlation between quantitative ability and exam scores.
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 Analysis of covariance (ancova) is used when you have two measurement variables and one attribute variable.
 In certain extreme cases, ANCOVA is less powerful than MannWhitney.
 In order to take account of the baseline score, when comparing the outcomes between the treatment groups, analysis of covariance (ANCOVA) was used.
 ANCOVA provides a mechanism for assessing the differences in dependent variable scores after statistically controlling for the covariate.
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 If I use a dummy code for the covariate (0 and 1; 1 and 2, etc) the repeated measures ANCOVA wants to analyze those as numeric.
 Analysis of covariance (ANCOVA) is applied only in those cases where the balanced independent variable is measured on a continuous scale.
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 A more detailed analysis of residuals can be found in the tutorial on ANCOVA.
 Headrick, T. C. (1997). Type I error and power of the rank transform analysis of covariance (ANCOVA) in a 3 x 4 factorial layout.
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 This broad class of models includes ordinary regression and ANOVA, as well as multivariate statistics such as ANCOVA and loglinear regression.
 A variable used in multiple regression analysis or ANCOVA that is controlled for.
 When I understand ANCOVA and main effect better, I'll make separate entries for them.
 In a tutorial on ANCOVA the Gender is added to the model to improve the quality of the fit.
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 If instead all 3 of these regression coefficients are zero the traditional ANCOVA design should be used.
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 If the slopes are not significantly different, the next step in an ancova is to draw a regression line through each group of points, all with the same slope.
 The first step in performing an ancova is to compute the regression line for each value of the attribute variable.
 Like any statistical procedure, the interpretation of ANCOVA depends on certain assumptions about the data entered into the model.
 Alternatives to traditional ANCOVA are also presented when circumstances in which certain assumptions have not been met.
 How is GLM ANCOVA different from traditional ANCOVA? The traditional method assumes that the covariates are uncorrelated with the factors.
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 In traditional ANCOVA, the independents area assumed to be orthogonal to the factors.
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 ANCOVA, or an alysis of cova riance is a test in statistics that is often implemented in computing packages.
 As a precursor to the ANCOVA, let us perform a betweengroups t test to examine overall differences between the two groups on the final exam.
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 Analysis of covariance (ANCOVA) is applied when an independent variable has a powerful correlation with the dependent variable.
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 Specifically, ANCOVA, just like ANOVA, assumes that the dependent variable is normally distributed and the independent variable(s) must be orthogonal.
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 Notably, in these cases, the estimate of treatment effect provided by ANCOVA is of questionable interpretability.
 ANCOVA tests whether certain factors have an effect after removing the variance for which quantitative predictors (covariates) account.
 Analysis of covariance (ANCOVA) was used to test the treatment effect of pyridoxine.
 This method is equivalent to an Analysis of Covariance (ANCOVA), although ANCOVA can be extended to more complicated situations.
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 The Analysis of covariance (ANCOVA) assumes that the regression coefficients in every group of the independent variable must be homogeneous in nature.
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 Analysis of covariance (ANCOVA) allows a dependent variable to be compared between groups at a common value of a covariate.
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 To account for the negative correlation between PFM width and age, we employed analysis of covariance (ANCOVA) with age as the covariate.
 If the covariate is related to the dependent in a linear manner, ANCOVA will be more powerful than ANOVA with blocking and is preferred.
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 ANOVA, ANCOVA and REGRESSION are all special cases of glm.
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 Some of the commonly used parametric techniques for analyzing quantitative data include the t test for means, ANOVA, ANCOVA, MANOVA, and the t test for r.
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 For example, in linear regression, in ANOVA or in ANCOVA the errors of the model are assumed to follow a normal distribution.
Categories
 Anova
 Science > Mathematics > Statistics > Covariance
 Science > Mathematics > Statistics > Covariate
 Science > Mathematics > Statistics > General Linear Model
 Glossaries > Glossary of Statistics /

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