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.
EdX
Dive into the world of statistical analysis with 'Basics of Statistical Inference and Modelling Using R'. This course will equip you with a strong foundation in understanding why certain statistical methods work, how to implement them using R, and when to apply them. It's an essential step for anyone looking to delve deeper into data science.
EdX
Expand your understanding of statistical analysis by delving into Advanced Statistical Inference and Modelling Using R. This course is designed for those who have a foundational grasp of linear regression and wish to apply their skills to more complex scenarios involving binary, count, or categorical responses, along with hierarchical experiments. Learn from experts and enhance your ability to perform advanced statistical analyses using the powerful programming language R.