Ementorhub

What you'll learn

Use statistical functions in RStudio to solve problems related to discrete and continuous probability distributions.

Create simple linear, polynomial, and multilinear regression models in RStudio and use those models to make predictions.

Perform one-sample and two-sample hypothesis tests and create confidence and prediction intervals on various statistics.

Skills you'll gain

Statistical Programming

Statistics

Statistical Hypothesis Testing

R Programming

Data Structures

Statistical Analysis

Regression Analysis

Statistical Methods

Probability Distribution

Data Manipulation

Data Import/Export

Statistical Modeling

Analysis

Plot (Graphics)

Descriptive Statistics

There are 6 modules in this course

The purpose of this course is to teach learners how to use the popular open-source (and thus, free) integrated development environment RStudio to perform basic and complex statistical calculations. After an introduction to basic calculations, vector, matrices, data frames, and how to import data from common file types (.xlsx, .csv, .txt), learners are taught how to solve probability and counting problems in R, followed by discrete and continuous probability distribution calculations, one-sample hypothesis tests, and two-sample hypothesis tests (comparisons). Finally, participants will learn how to create regression models in R and perform analysis of variance (ANOVA). One of the most beneficial aspect of the course are the programming assignments, which are completed online in the R programming language in Jupyter notebooks.

Descriptive Statistics and Graphical Presentation of Data

Counting Techniques and Probability Distribution Functions

One-Sample Hypothesis Testing

Two-Sample Hypothesis Tests

Regression and ANOVA