This course will consist of instructional videos for statistical concepts broken down into manageable chunks – each followed by some guided questions to help your understanding of the topic. Most weeks, the instructional section will be followed by tutorial videos for using R, which we’ll then apply to a hands-on Lab where we will answer a specific question using real-world datasets.
We’ll cover basic Descriptive Statistics in our first “Unit” – learning about visualizing and summarizing data. Unit two will be a “modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We’ll learn how to interpret and use those functions with a little bit of Pre-Calculus (but we’ll keep it very basic). Finally in the third Unit, we’ll learn about Inferential statistical tests such as the t-test, ANOVA, and chi-square.
This course is intended to have the same “punch” 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).
I hope you’ll join me 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?
WAYS TO TAKE THIS EDX COURSE:
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Michael J. Mahometa
Michael J. Mahometa, Ph.D. – Lecturer and Senior Statistical Consultant for The University of Texas at Austin’s Department of Statistics and Data Sciences.
Michael received his Ph.D. in Psychology from The University of Texas at Austin in 2006. His major course work was completed in Behavioral Neuroscience, with a minor in Statistics. It was during that time he realized he had a love of answering questions using the statistical techniques he was learning. He also realized that he very much enjoyed teaching. After graduating he quickly turned his passion for both teaching and statistics into a position as a Statistical Consultant for The University of Texas. Since that time he’s helped develop new statistics courses to take advantage of new teaching techniques, he’s helped graduate students with their research questions, and he’s helped undergraduate students learn this amazing tool called data analysis.
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
Week Three: Bivariate Distributions
- The scatterplot
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
- The Logistic function model
- Picking a good model
Week Seven: Sampling
- The sampling distribution
- Central limit theorem
- Confidence intervals
Week Eight: 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 Nine: Hypothesis Testing (Categorical Data)
- The chi-square test
Week Ten: Hypothesis Testing (More Than Two Group Means)
- The ANOVA
- One-way ANOVA
- Two-way ANOVA