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Princeton University

Statistics One

Princeton University via Coursera

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Overview

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. 

Syllabus

Lecture Topics
  • 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 Topics:
  • 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) 

Taught by

Andrew Conway

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Reviews

3.3 rating, based on 18 Class Central reviews

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  • Anonymous
    Prof. Conway clearly knows his stuff, and I am pretty sure that his teaching works for his Princeton psychology students, but in my opinion this course should be avoided by most other students. To start with the title suggests that it is a general i…
  • Anonymous
    I've now taken two courses at Coursera and I gave the other one a 5 star rating. Sorry, I can only give this one two stars. Prof. Conway obviously knows his stuff and his teaching style may be well suited to traditional learning but this course is supposed to be a MOOC and a lot more thought needs to go into presenting this course in this medium. Not enough time is given to explaining concepts. A huge amount of time is spent with Prof Conway presenting formulas to the class via Power Point presentations. Hasn't anyone at Princeton read what Edward Tufte has to say about Power Point? Also, there was too much time spent sharing baseball stats anecdotes - time that could have been spent explaining the math behind the formulas.
  • Anonymous
    Very informative class. Professor Conway knows his subject very well. Media presentation is excellent. Unfortunately, class is skewed toward psychological studies and becomes very difficult to follow somewhere after equator.
  • Anonymous
    Conway may know his stats, but this class is certainly not for everyone. Concepts should be explained in much greater depth. This is really just a surface scratching that is not yet thoroughly developed.
  • Preetha
    Can anyone help me understand how to start this course? I selected "Go To Class" and I dont see any option to play the sessions in Coursera link.
  • Anonymous
    really the best statistics instructor ever! Really clear and bright, very helpful!
  • Anonymous
    Not recommend anyone to take it, unless you are a psy student and had stat before.
    This course is Only appropriate for social students to refresh their mind on stat, that's all.
    So much important stat concepts are not even mentioned in the course and for those concepts that mentioned, no detailed math/stat principle explained.

    Cnt believe this is a Princeton version.
  • Leonardo Alvarado
    I think this an excellent course for people with few knowledge in statistics. R is an excellent language for this topic
  • Sašo Karakatič
  • Anonymous
  • Revanth
  • Ankit Kamboj
  • Mia Rogelsek
  • Colin Khein
  • Elizabeth Jones
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