## Introduction to Statistics with R Programming

An overview of the fundamental principles and techniques of statistics using the R programming language. This course aims to provide a comprehensive overview of statistical concepts and methods as well as skills in using statistical software.

The course is segmented into three sections, Descriptive Statistics, Inferential Statistics, and Regression. In the first section, we will cover topics such as Data Types, Probability Concepts, Common Statistical Measures, Skewness & Kurtosis, and Plotting Data. In the next section on Inferential statistics, we will use our knowledge from the previous section to gain an in-depth understanding of fundamental Inferential Statistics concepts such as the Central Limit Theorem, Hypothesis Testing, Confidence Intervals, Z-statistic & T-statistic, One-sample T-tests, and Two-sample T-tests. We will then finish up the course by talking about Correlation, Simple Linear Regression, and Multiple Regression.

Most of the concepts in this course are followed up with a coding example in R. R is a programming language and a free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. It is widely used among statisticians and data miners for developing statistical software and data analysis. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible.

In conclusion, the Introduction to Statistics course is designed to give students a foundational understanding of statistics, as well as to help students become proficient in using the R programming language. This course is suitable for anyone who wants to understand statistical analysis and its applications or is looking to pursue a career in data science or machine learning. With this course, students will develop skills in statistical analysis, interpretation, and communication, making them well-prepared for further statistical education and analysis

What you’ll learn:

- Define and classify data types and apply appropriate statistical measure
- Apply probability concepts and understand its relation to statistical analyses
- Perform hypothesis testing, calculate confidence intervals, and differentiate between a Z-statistic and T-statistic
- Apply regression models in practice

#statistical #r #datascience