Data Analysis and Statistical Inference

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Course Date: 01 September 2014 to 10 November 2014 (10 weeks)

Price: free

Course Summary

This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

Estimated Workload: 8-10 hours/week

Course Instructors

Mine Çetinkaya-Rundel

Mine Çetinkaya-Rundel is an Assistant Professor of the Practice at the Department of Statistical Science at Duke University. She received her Ph.D. in Statistics from the University of California, Los Angeles, and a B.S. in Actuarial Science from New York University's Stern School of Business.

Dr. Çetinkaya-Rundel is primarily interested in innovative approaches to statistics pedagogy. Some of her recent work focuses on developing student-centered learning tools for introductory statistics courses, teaching computation at the introductory statistics level with an emphasis on reproducibility, and exploring the gender gap in self-efficacy in STEM fields. Her research interests also include spatial modeling of survey, public health, and environmental data. She is a co-author of OpenIntro Statistics and a contributing member of the OpenIntro project, whose mission is to make educational products that are open-licensed, transparent, and help lower barriers to education. She is also a co-editor of the Citizen Statistician blog and a contributor to the Taking a Chance in the Classroom column in Chance Magazine.

Course Description

The goals of this course are as follows:
  1. Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
  2. Use statistical software (R) to summarize data numerically and visually, and to perform data analysis.
  3. Have a conceptual understanding of the unified nature of statistical inference.
  4. Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions.
  5. Model and investigate relationships between two or more variables within a regression framework.
  6. Interpret results correctly, effectively, and in context without relying on statistical jargon.
  7. Critique data-based claims and evaluate data-based decisions.
  8. Complete a research project that employs simple statistical inference and modeling techniques.


Will I get a Certificate after completing this class?
Yes, if you successfully complete the class according to the grading policy.

Is this course free?
Yes! You can take this course for free, and you can earn a certificate for free.

However there is a charge if you would like a verified certificate. You can read more about the verified certificate here, and decide if this is something you are interested in. In addition, if you would like a Specialization Certificate in Reasoning, Data Analysis and Writing, you will need a verified certificate. You can read more about the Specialization here.

Do I need prior programming knowledge to take this course? 

No programming background is required. Everything you need to know will be covered in the course. 

What resources will I need for this class?
You will need a stable internet connection in order to watch the videos, access the suggested readings, take the quizzes and tests. To complete the labs and the capstone project, you will need to run R (a free software environment for statistical computing and graphics) and RStudio (an integrated development environment that serves as a user interface for R).

Alternatively, you will have the option of completing the labs (though not the project) on, an online interactive learning platform. The platform hosts interactive versions of the R labs used in this course, and it provides you with hints and instant feedback that should facilitate the learning process. The hints are like having a tutor with you, and the instant feedback helps avoid much of the frustration associated with learning a new programming language and deciphering cryptic error messages.

Can R/RStudio run on any system? 
R is available for Windows, Mac OS, and Ubuntu.  You can download R at, and RStudio at Once the course begins we will post videos on installing and getting started with R and RStudio.

Do I have to learn R to complete this course?
While you can navigate through some of the course material without using R, you will need R for the labs and the capstone project to be eligible for a Certificate with Distinction.

Can R run on 
smartphone or a tablet? 
It is possible to run R on a smartphone or a tablet (see here), but this will require access to a working install of RStudio server, which we will not be providing or supporting for this course. Instructions for installing RStudio server are available at the RStudio website here


Week 1: Unit 1 - Introduction to data
  • Part 1 – Designing studies
  • Part 2 – Exploratory data analysis
  • Part 3 – Introduction to inference via simulation
Week 2: Unit 2 - Probability and distributions
  • Part 1 – Defining probability
  • Part 2 – Conditional probability
  • Part 3 – Normal distribution
  • Part 4 – Binomial distribution
Week 3: Unit 3 - Foundations for inference
  • Part 1 – Variability in estimates and the Central Limit Theorem
  • Part 2 – Confidence intervals
  • Part 3 – Hypothesis tests
Week 4: Finish up Unit 3 + Midterm
  • Part 4 – Inference for other estimators
  • Part 5 - Decision errors, significance, and confidence
Week 5: Unit 4 - Inference for numerical variables
  • Part 1 – Comparing two means
  • Part 2 – Bootstrapping
  • Part 3 – Inference with the t-distribution
  • Part 4 – Comparing three or more means (ANOVA)
Week 6: Unit 5 - Inference for categorical variables
  • Part 1 – Single proportion
  • Part 2 – Comparing two proportions
  • Part 3 – Inference for proportions via simulation
  • Part 4 – Comparing three or more proportions (Chi-square)
Week 7: Unit 6 - Introduction to linear regression
  • Part 1 – Relationship between two numerical variables
  • Part 2 – Linear regression with a single predictor
  • Part 3 – Outliers in linear regression
  • Part 4 – Inference for linear regression
Week 8: Unit 7 - Multiple linear regression
  • Part 1 – Regression with multiple predictors
  • Part 2 – Inference for multiple linear regression
  • Part 3 – Model selection
  • Part 4 – Model diagnostics
Week 9: Review / catch-up week
  • Bayesian vs. frequentist inference
Week 10: Final exam


The class will include video lectures, between 5 and 10 minutes in length, containing a few quiz questions per video. There will be homework assignments consisting of graded multiple choice quizzes and optional, ungraded questions from the textbook, computational data analysis assignments, a data analysis project, a midterm, and a final exam.

Suggested Reading

Lectures are designed to be self-contained, but we recommend that students refer to the book OpenIntro Statistics (Second Edition). The course will closely follow this book, and hence the text can serve as supplementary material to the videos. In addition, practice problems will be assigned from the book. The book is open-source and freely available online at

Course Workload

8-10 hours/week

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