Statistics One is a comprehensive yet friendly introduction to statistics.

Statistics One is designed to be a comprehensive yet friendly introduction to fundamental concepts in statistics. Comprehensive means that this course provides a solid foundation for students planning to pursue more advanced courses in statistics. Friendly means exactly that. The course assumes very little background knowledge in statistics and introduces new concepts with several fun and easy to understand examples.

This course is, quite literally, for everyone. If you think you can't learn statistics, this course is for you. If you had a statistics course before but feel like you need a refresher, this course is for you. Even if you are a relatively advanced researcher or analyst, this course provides a foundation and a context that helps to put one’s work into perspective.

Statistics One also provides an introduction to the R programming language. All the examples and assignments will involve writing code in R and interpreting R output. R software is free! What this means is you can download R, take this course, and start programming in R after just a few lectures. That said, this course is not a comprehensive guide to R or to programming in general.

- Lecture 1: Experimental research

- Lecture 2: Correlational research

- Lecture 3: Variables and distributions

- Lecture 4: Summary statistics

- Lecture 5: Correlation

- Lecture 6: Measurement

- Lecture 7: Introduction to regression

- Lecture 8: Null Hypothesis Significance Tests (NHST)

- Lecture 9: Central limit theorem

- Lecture 10: Confidence intervals

- Lecture 11: Multiple regression

- Lecture 12: Multiple regression continued

- Lecture 13: Moderation

- Lecture 14: Mediation

- Lecture 15: Group comparisons (t-tests)

- Lecture 16: Group comparisons (ANOVA)

- Lecture 17: Factorial ANOVA

- Lecture 18: Repeated measures ANOVA

- Lecture 19: Chi-square

- Lecture 20 Binary logistic regression

- Lecture 21: Assumptions revisited (correlation and regression)

- Lecture 22: Generalized Linear Model

- Lecture 23: Assumptions revisited (t-tests and ANOVA)

- Lecture 24: Non-parametrics (Mann-Whitney U, Kruskal-Wallis)

- Lab 1: Download and install R

- Lab 2: Histograms and summary statistics

- Lab 3: Scatterplots and correlations

- Lab 4: Regression

- Lab 5: Confidence intervals

- Lab 6: Multiple regression

- Lab 7: Moderation and mediation

- Lab 8: Group comparisons (t-tests, ANOVA, post-hoc tests)

- Lab 9: Factorial ANOVA

- Lab 10: Chi-square

- Lab 11: Non-linear regression (Binary logistic and Poisson)

- Lab 12: Non-parametrics (Mann-Whitney U and Kruskal-Wallis)

**What resources do I need for this class?**All you need is an internet connection!

**Does Princeton award credentials or reports regarding my work in this course**No certificates, statements of accomplishment, or other credentials will be awarded in connection with this course.

**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.

**Do I need to use R for this course?**Yes, all labs and assignments will be designed to run in R.

**Can R run on any system?**R is available for Windows, Mac OS, and Ubuntu. Regardless of your operating system, you shouldn't have any problem with the material of this course. http://www.r-project.org

**Can R run on iPhone/iPad?**Although it can run with limited success on an iPhone/iPad, a user must jailbreak the device to do so which will invalidate any warranty. Read more.

**Which time zone is officially used?**All deadlines are based on EDT (Eastern Daylight Time).

**How do I ask more questions?**Due to the number of students enrolled in the course (estimated to be approximately 100,000) the best way to ask more questions is to post your question on the discussion forums. The discussion forums will be activated on September 8th, two weeks prior to the start of the course.

Dates:

- June 2015, 12 weeks
- 22 September 2013, 12 weeks
- 3 September 2012, 6 weeks

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