In the previous exercise on the #REcology series, we learned how to define the impact of one explanatory variable to another response variable. In a real practice, particularly in experimental or observational design, explanatory variables are often found to be more than one. Thus, it needs a new determination to analyze the data-set and generate the correct conclusion. In this exercise, we will try to do an analysis of the co-variance (ANCOVA) method. Covariates here refers to the continuous explanatory variables. It involves a combination of regression and analysis of variance. ANCOVA requires a continuous response variable, at least one continuous explanatory, and at least one explanatory factor variable. Answers to these exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page. Data-set on this exercise can be downloaded here.
Load the data-set and required package,
Do some plotting; what can be inferred? Create a basic verbal hypothesis.
Create an interaction model based on the basic verbal hypothesis generated on Exercise 2.
Check the interaction between the explanatory variables of the model created using ANOVA. Make sure that the interaction of those two variables is insignificant.
Check the statistic summary of the model. Pay attention to the intercept, slope, and the R square of the model.
Create a linear regression plot and determine the equation based on the statistic summary.