Statistics One

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)