Statistics One

所在平台: CourseraArchive

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

课程主页: https://www.coursera.org/course/stats1

课程评论: 4 个评论

评论课程关注课程

课程简介

Statistics One is a comprehensive yet friendly introduction to statistics.

课程大纲

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)

课程评论(4条)

0	都柏林的老菲利普 2014-01-10 08:56 0 票支持; 0 票反对不过整体上对于了解一下statistics和相关的应用还是很好的

0	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。

0	申砾 2013-10-20 23:05 0 票支持; 0 票反对教授讲的很清晰，有不少例子，一些基本概念也会详细讲解，学了之后对R语言也算是入门了。另外课程的forum很活跃，看大家的讨论也收获不少。

0	宋鑫要学习 2013-10-12 13:41 0 票支持; 0 票反对刚上了两次课，讲的都是很简单的概念，对想从事机器学习/数据挖掘而又缺乏统计知识的同学是个不错的选择。另外，这门课的lab用的是R语言，对想要入门R的同学也是一个不错的选择

课程详情

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.