Statistical Analysis in R Professional Certificate
What you will learn:
Basics of statistical inference, confidence intervals and hypothesis testing. Commonly used tests. Pvalues, statistical and practical significance.
Analysis of Variance (ANOVA) and post-hoc tests. Diagnostics, implementation and interpretation using R.
Numerical Methods: The use of simulations, nonparametric bootstrap and permutation tests using R.
Linear Regression, Analysis of Variance with Covariates (ANCOVA), Generalised Linear Models (GLMs) and Mixed Effects Linear models using R.
Basics of power analysis (sample size evaluation) and some thoughts on experimental design.
Extend your knowledge of linear regression to the situations where the response variable is binary, a count, or categorical as well as to hierarchical experimental set-up. Advanced Statistical Inference and Modelling Using R is part two of the Statistical Analysis in R professional certificate. This course is directed at [...]
Learn why a statistical method works, how to implement it using R and when to apply it and where to look if the particular statistical method is not applicable in the specific situation. Basics of Statistical Inference and Modelling Using R is part one of the Statistical Analysis in [...]