MEDSCI 725: EXPERIMENTAL DESIGN
Ellynn Sy
ANCOVA and More Complex Designs (Parametric Tests)
Introduction
Analysis of covariance (ANCOVA) is most frequently used to refer to the statistical technique that combines regression and ANOVA [1]. It offers a way to obtain a more precise assessment of the effect of the experimental manipulations on the dependent variable. An ANCOVA design requires the measurement of one or more other variables. These variables are covariates, predictor, variables, concomitant variables, or control variables. It represent sources of variation that are thought to influence the dependent variable. But it does not get controlled by the experiment procedures [2]. ANCOVA has the same assumptions as any linear model …show more content…
The treatments are allocated such that each treatment occurs only once in a row and in a column. It is assumed that rows and columns are equal that is why the arrangement will form a square. However, LSD is not flexible compared to the randomized block design. The number of treatments is limited to the number of rows and columns. As you increase the size of the square you formed, the experimental error will increase as well. That is why LSD is rarely used if there are more than 12 treatments involved. It is also not suitable to be used when there are less than 5 treatments involved because there is few degrees of freedom for error and the estimate of error variance will not be precise …show more content…
Then the data was graphed. Provided that the linear regression was significantly different from zero, the parameters that describe the 1st order least-squares line-of-best-fit were recorded. Then the null hypothesis that the 1st order sample regression coefficients arise from the same population was tested. Analysis of covariance (ANCOVA) was performed. The dependent variable as a function of the covariate for each independent variable was
Ellynn Sy
ANCOVA and More Complex Designs (Parametric Tests)
Introduction
Analysis of covariance (ANCOVA) is most frequently used to refer to the statistical technique that combines regression and ANOVA [1]. It offers a way to obtain a more precise assessment of the effect of the experimental manipulations on the dependent variable. An ANCOVA design requires the measurement of one or more other variables. These variables are covariates, predictor, variables, concomitant variables, or control variables. It represent sources of variation that are thought to influence the dependent variable. But it does not get controlled by the experiment procedures [2]. ANCOVA has the same assumptions as any linear model …show more content…
The treatments are allocated such that each treatment occurs only once in a row and in a column. It is assumed that rows and columns are equal that is why the arrangement will form a square. However, LSD is not flexible compared to the randomized block design. The number of treatments is limited to the number of rows and columns. As you increase the size of the square you formed, the experimental error will increase as well. That is why LSD is rarely used if there are more than 12 treatments involved. It is also not suitable to be used when there are less than 5 treatments involved because there is few degrees of freedom for error and the estimate of error variance will not be precise …show more content…
Then the data was graphed. Provided that the linear regression was significantly different from zero, the parameters that describe the 1st order least-squares line-of-best-fit were recorded. Then the null hypothesis that the 1st order sample regression coefficients arise from the same population was tested. Analysis of covariance (ANCOVA) was performed. The dependent variable as a function of the covariate for each independent variable was