BibTex Citation Data :
@article{J.Gauss6485, author = {Vina Fitri and Triastuti Wuryandari and Diah Safitri}, title = {PENDUGAAN DATA HILANG PADA RANCANGAN ACAK KELOMPOK LENGKAP DENGAN ANALISIS KOVARIAN}, journal = {Jurnal Gaussian}, volume = {3}, number = {3}, year = {2014}, keywords = {Missing data; Analysis of Covariance (ANCOVA); Randomized Complete Block Design (RCBD); Analysis of Variance (ANOVA)}, abstract = { Analysis of Covariance (ANCOVA) is mostly used in the analysis of research or experimental design. ANCOVA is the combination between regression analysis and Analysis of Variance (ANOVA). ANCOVA were used because there are some concomitant variable, which is variable that difficult to control by the researchers but an impact on observed the response variable. The purpose from concomitant variable is reduces variability in the experiment. If there is missing data on Randomized Complete Block Design (RCBD) the first must be done estimating the missing data before ANCOVA done. ANCOVA on RCBD with complete data or missing data isn’t much different, if there are missing data, the degrees of freedom is reduced by the total amount of missing data and the sum of square treatment reduced by the value of the bias. Application of tensile strength of the glue experiment to the case ANCOVA on RCBD with one missing data show no effect of treatment and group by the tensile strength of the glue. For Fe toxicity experiment with two missing data are found only treatment effect to Fe texicity. Based on value from the coefficient of variance for one missing data and two missing data showed that ANCOVA is more appropriately used than ANOVA. }, issn = {2339-2541}, pages = {499--508} doi = {10.14710/j.gauss.3.3.499-508}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/6485} }
Refworks Citation Data :
Analysis of Covariance (ANCOVA) is mostly used in the analysis of research or experimental design. ANCOVA is the combination between regression analysis and Analysis of Variance (ANOVA). ANCOVA were used because there are some concomitant variable, which is variable that difficult to control by the researchers but an impact on observed the response variable. The purpose from concomitant variable is reduces variability in the experiment. If there is missing data on Randomized Complete Block Design (RCBD) the first must be done estimating the missing data before ANCOVA done. ANCOVA on RCBD with complete data or missing data isn’t much different, if there are missing data, the degrees of freedom is reduced by the total amount of missing data and the sum of square treatment reduced by the value of the bias. Application of tensile strength of the glue experiment to the case ANCOVA on RCBD with one missing data show no effect of treatment and group by the tensile strength of the glue. For Fe toxicity experiment with two missing data are found only treatment effect to Fe texicity. Based on value from the coefficient of variance for one missing data and two missing data showed that ANCOVA is more appropriately used than ANOVA.
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