Basics of Statistical Inference and Modelling Using R

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Basics of Statistical Inference and Modelling Using R is part one of the Statistical Analysis in R professional certificate.

This course is directed at people with limited statistical background and no practical experience, who have to do data analysis, as well as those who are "out of practice". While very practice oriented, it aims to give the students the understanding of why the method works (theory), how to implement it (programming using R) and when to apply it (and where to look if the particular method is not applicable in the specific situation).

What you will learn

• Sample and population. Sampling distribution. Parameter estimates and confidence intervals.
• Central Limit Theorem
• Hypothesis Testing. P-values. Standard tests: t-test, the test of binomial proportions, Chi-squared test. Statistical and Practical Significance.
• Exploratory data analysis and data visualisation using R.
• Analysis of Variance (ANOVA) and post-hoc tests using R.
• Multivariate analysis using linear regression and analysis of variance with covariates (ANCOVA). Assumptions, diagnostics, interpretation. Model comparison and selection.
• Numerical Methods: The use of simulations, non-parametric bootstrap and permutation tests using R.
• Identifying the research question.
• Experimental design (basics of power analysis) and missing data.

Skills you learn

• Data Analysis
• Statistical Analysis
• Statistical Inference

Auto Summary

"Basics of Statistical Inference and Modelling Using R" is an introductory course tailored for individuals with limited statistical knowledge and no prior practical experience in data analysis, as well as for those seeking to refresh their skills. This course is part of the Statistical Analysis in R professional certificate offered by edX. Focused on the domain of Maths & Statistics, the course provides a comprehensive understanding of statistical inference and modelling techniques using R programming. It emphasizes practical application while ensuring learners grasp the underlying theory, implementation methods, and appropriate contexts for various statistical techniques. The course is designed to be highly accessible, making it ideal for beginners or those looking to re-engage with statistical practices. Although the exact duration is not specified, learners can subscribe to the professional track for a structured and detailed learning experience. This course is particularly suited for individuals seeking to enhance their data analysis capabilities in a practical and theoretically sound manner.