https://media.springernature.com/full/nature-static/assets/v1/image-assets/485176a-f1.2.jpg
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.
Exercise 1
Load the data-set and required package, car.
Exercise 2
Do some plotting; what can be inferred? Create a basic verbal hypothesis.
Exercise 3
Create an interaction model based on the basic verbal hypothesis generated on Exercise 2.
Exercise 4
Check the interaction between the explanatory variables of the model created using ANOVA. Make sure that the interaction of those two variables is insignificant.
Statistics with R – Intermediate Level. In this course, you will learn how to:
- Run parametric correlation and t-tests
- Learn about two-way and three-way analysis of variances
- And much more
Exercise 5
Check the statistic summary of the model. Pay attention to the intercept, slope, and the R square of the model.
Exercise 6
Create a linear regression plot and determine the equation based on the statistic summary.
Leave a Reply