# Basics of Statistical Inference and Modelling Using R

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.

### There is one session available:

After a course session ends, it will be archived.
Starts Dec 8
Estimated 6 weeks
5–10 hours per week
Self-paced
Free

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).

### At a glance

• Institution: UCx
• Subject: Math
• Level: Intermediate
• Prerequisites:

The course requires a good understanding of basic algebra, logarithms and exponential functions, the equation of a straight line, basic concepts of probability, continuous and discrete random variables, distributions, probability (density) functions, cumulative probability density functions, expectation and variance and basic sample statistics.

If you have never used R before, it is also recommended to go through the chapters 1-7, and 9 of the R-manual found at https://cran.r-project.org/doc/manuals/r-release/R-intro.html

# What you'll learn

Skip What you'll 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.