Use R to learn the fundamental statistical topic of basic inferential statistics. In the second part of a two part course, we’ll learn how to take data and use it to make reasonable and useful conclusions. You’ll learn the basics of statistical thinking – starting with an interesting question and some data.
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Then, we’ll apply the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion.
In this second of a two part statistics course, we will cover basic Inferential Statistics – integrating ideas of Part 1. If you have a basic knowledge of Descriptive Statistics, this course is for you. We will learn how to sample data, examine both quantitative and categorical data with statistical techniques such as t-tests, chi-square, ANOVA, and Regression.
Both parts of the course are intended to cover the same material as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R).
This course will consist of:
Instructional videos for statistical concepts broken down into manageable topics.
Guided questions to help your understanding of the topic.
Weekly tutorial videos for using R. Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets. Weekly wrap-up questions challenging both topic and application knowledge.
Join us in learning how to look at the world around us. What are the questions? How can we answer them? And what do those answers tell us about the world we live in?
What you'll learn:
- How to utilize samples of data
- Basic R programming (guided through tutorials)
- Basic Inferential Statistics including t-tests, chi-square, ANOVA, and regression
