Likan Zhan

R for Statistics and Data Visualization

1. Lecture Time and Location

2. Prerequisites

Students of this course should be familiar with the basic concepts used in statistics, such as Mean, Standard Deviation, Normal Distribution, t-statistic, ANOVA, F-ratio, p-value, Hypothesis Testing etc. To acheive this, Students should have already finished some introductory courses in statistics, such as Statistics for the Behavioral Sciences, or other courses at the same level. Students can also learn these basic concepts by themselves.

3. Course Information

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories by John Chambers and colleagues. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

Linear models, their variants, and extensions are among the most useful and widely used statistical tools for social research. This course aims to provide an accessible, in-depth, modern treatment of regression analysis, linear models, generalized linear models, and closely related methods.

4. Syllabus and Lecture Notes

5. References

6. Final Examination

---
title: "Exam Sample"
author: "张三-20181030"    # <- Your name and student number
lastmod: "2018-12-14"
date: '2018-10-30'
output:
  html_document
---                       # <- You can write any text after this
a. You can add any text here.

```{r}
# You can add comments here
str(Titanic) # <- This should be eligible R code.
```
b. You can also add any text here.
install.packages("rmarkdown", dependencies = TRUE)
rmarkdown::render(
  "directory/to/your/file/ZhangSan_20170708.rmd",
  output_format = "html_document")

7. Tools