• Length:
6 Weeks
• Effort:
3–6 hours per week
• Price:

FREE
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• Institution
• Subject:
• Level:
Introductory
• Language:
English
• Video Transcript:
English

## Prerequisites

• Basic math – arithmetic and algebra
• Students should be comfortable solving math problems such as: 25 = 15 + 2x
• Comfort with a computer and comfort using a novel computer software

In this first part of a two part course, we’ll walk through the basics of statistical thinking – starting with an interesting question. Then, we’ll learn 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.

This course will consist of:

• Instructional videos for statistical concepts broken down into manageable topics
• 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

We will cover basic Descriptive Statistics – learning about visualizing and summarizing data, followed by a “Modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We will learn how to interpret and use those functions with basic Pre-Calculus. These two “units” will set the learner up nicely for the second part of the course: Inferential Statistics with a multiple regression cap.

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).

With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

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

Skip What you'll learn
• Descriptive Statistics
• How to visualize data
• Data structure and how to examine it
• Basic R programming (guided through tutorials)
• Simple modeling of linear, exponential, and logistic growth

### Syllabus

Skip Syllabus
Week One: Introduction to Data
• Why study statistics?
• Variables and data
• Getting to know R and RStudio
Week Two: Univariate Descriptive Statistics
• Graphs and distribution shapes
• Measures of center and spread
• The Normal distribution
• Z-scores
Week Three: Bivariate Distributions
• The scatterplot
• Correlation
Week Four: Bivariate Distributions (Categorical Data)
• Contingency tables
• Conditional probability
• Examining independence
Week Five: Linear Functions
• What is a function?
• Least squares
• The Linear function – regression
Week Six: Exponential and Logistic Function Models
• Exponential data
• Logs
• The Logistic function model
• Picking a good mode

Michael J. Mahometa
Lecturer and Senior Statistical Consultant
University of Texas at Austin

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