Analysis of Variance (ANOVA)
Model an Analysis of Variance (ANOVA) between a numeric response variable and one or more predictor variables.
This module computes an Analysis of Variance (ANOVA), modelling one dependent variable against one or more independent variables. ANOVA is often used in order to test the differences between two or more means.
Whilst the ANOVA model will inform you whether at least one of the population means is different from at least one other mean, it will not tell you which mean is different. For this, Tukey Honest Significant Difference (TukeyHSD) should be used upon the ANOVA model.
In the example below, we use the ANOVA module to find out whether there is any significant difference between CO2 uptake in plants that received chilled and nonchilled treatment.
The output below shows the ANOVA results. It also includes a graph of residuals plotted against the fitted values as well as an ANOVA summary table.
The ANOVA summary table indicates that there are significant differences between different treatment groups as its p-value is smaller than 0.05. More details on the output can be found in the R documentation for
|outcome_var||Yes||Column with data type one of: Decimal, Integer||The dependent variable to be modeled by the selections in |
|model_var1||Yes||Any column other than the column chosen for outcome_var.||The first independent variable or predictor variable to include in the modeled ANOVA.|
|model_var2||No||Any column other than the column chosen for outcome_var.||An optional second predictor variable.|
|model_var3||No||Any column other than the column chosen for outcome_var.||An optional third predictor variable.|
|model_var4||No||Any column other than the column chosen for outcome_var.||An optional fourth predictor variable.|
|model_var5||No||Any column other than the column chosen for outcome_var.||An optional fifth predictor variable.|