Statistics One

开始时间: 待定 持续时间: Unknown

所在平台: Coursera

课程类别: 统计和数据分析

大学或机构: Princeton University(普林斯顿大学)

授课老师: Andrew Conway



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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 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: Sampling 
  • Lecture 10: Central limit theorem 
  • Lecture 11: Multiple regression 
  • Lecture 12: The General Linear Model (GLM) 
  • 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, box plots, and descriptives 
  • Lab 3: Scatterplots and correlation 
  • 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) 



都柏林的老菲利普 2014-01-10 08:56 0 票支持; 0 票反对



都柏林的老菲利普 2014-01-10 08:55 0 票支持; 0 票反对

Hypothesis testing那段的评论明显讲的有问题。


申砾 2013-10-20 23:05 0 票支持; 0 票反对



宋鑫要学习 2013-10-12 13:41 0 票支持; 0 票反对


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Statistics One is a comprehensive yet friendly introduction to statistics.


统计入门 统计 统计导论 统计学 统计上 统计学入门 统计学导论 R R语言 普林斯顿大学 ANOVA



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