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Foundations of Data Analysis - Part 2: Inferential Statistics

The University of Texas at Austin via edX

2 Reviews 658 students interested

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Overview

In the second part of a two part statistics 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. 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.

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

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?

Syllabus

Week One: Introduction to Data
  • Why study statistics?
  • Variables and data
  • Getting to know R and RStudio
Week Two: Sampling
  • Why study statistics?
  • The sampling distribution
  • Central limit theorem
  • Confidence intervals
Week Three: Hypothesis Testing (One and Two Group Means)
  • What makes a hypothesis test?
  • Errors in testing
  • Alpha and critical values
  • Single sample test
  • Independent t-test and Dependent t-test
Week Four: Hypothesis Testing (Categorical Data)
  • The chi-square test
  • Goodness-of-Fit
  • Test-of-Independence
Week Five: Hypothesis Testing (More Than Two Group Means)
  • The ANOVA
  • One-way ANOVA
  • Two-way ANOVA
Week Six: Hypothesis Testing (Quantitative data)
  • Correlation
  • Simple (single variable) regression
  • Multiple regression

Taught by

Michael J. Mahometa

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