In this thesis a analysis of covariance (ANCOVA) is presented, which is actually a special case of statistical modeling within a general linear model characterized by the existence of one or more dependent variables and one or more independent variables and integrates variance analysis (ANOVA) and regression analysis. Unlike ANOVAs here, as the independent variable, there is also a covariate that is in a certain linear relationship with the dependent variable and is included in the model in order to gain a greater precision of the statistical conclusion. ANCOVA is used to increase the sensitivity of the test, adjusting the expectation of the dependent variables to the level at which all subjects would have the same values in covariates and multivariate ANOVAs. Along with the standard assumptions for use of ANOVA with ANCOVA we have the assumptions of covariates reliability, the linearity between covariance pairs and between covariates and dependent variables and the homogeneity of regression. The hypothetical example shows all formulas within the model and calculates all values manually and using the Statistica software.