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R for Statistical Analysis

R for Statistical Analysis

This course will teach you how to use R for basic statistical procedures.


This course teaches R based on students’ existing knowledge of basic statistics. It does not treat statistical concepts in depth, but rather focuses on how to use R to perform basic statistical analysis including summarizing and graphing data, hypothesis testing, linear regressions and more. This course is appropriate for anyone who wants to gain a familiarity with R to conduct common statistical analyses, and for teachers who wish to use R in teaching introductory statistics.

  • Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

After completing this course, students will be able to use R to summarize and graph data, calculate confidence intervals, test hypotheses, assess goodness-of-fit, and perform linear regression. See our related course, “R Programming – Introduction 1,” for an introduction to programming in R.

  • Summarize and graph data
  • Calculate confidence intervals
  • Perform hypothesis tests
  • Assess goodness-of-fit
  • Perform linear regression
  • Take bootstrap samples

Who Should Take This Course

Anyone who wants to gain a familiarity with R to use it to conduct statistical analysis. Also, teachers who wish to use R in teaching introductory statistics.

Our Instructors

Dr. John Verzani

Dr. John Verzani

Dr. John Verzani is a Professor and Chair of the Mathematics Department at the College of Staten Island of the City University of New York. His research interests and publications are in the area of probability theory and superprocesses. He is active in the R community.

Course Syllabus

Week 1

The One Sample T-Test in R

  • A manual computation
    • A data vector
    • The functions: mean(), sd(), (pqrd)qnorm()
    • Finding confidence intervals
    • Finding p-values
    • Issues with data
      • Using data stored in data frames (attach()/detach(), with())
      • Missing values
      • Cleaning up data
  • EDA graphs
    • Histogram()
    • Boxplot()
    • Densityplot() and qqnorm()
  • The t.test() function
  • P-values
  • Confidence intervals
  • The power of a t test

Week 2

The Two Sample T-Tests, the Chi-Square GOF Test in R

  • GUI’s
    • Rcmdr
    • PMG
  • Tests with two data vectors x, and y
    • Two independent samples no equal variance assumption
    • Two independent samples assuming equal variance
    • Matched samples
    • Data stored using a factor to label one of two groups; x ~ f;
    • Boxplots for displaying more than two samples
    • The chisq.tests
      • Goodness of fit
      • Test of homogeneity or independence

Week 3

The Simple Linear Regression Model in R

  • The basics of the Wilkinson-Rogers notation: y ~ x
  • * y ~ x linear regressionScatterplots with regression lines
    • Scatterplots with regression lines
    • Reading the output of lm()
    • Confidence intervals for beta_0, beta_1
    • Tests on beta_0, beta_1
  • Identifying points in a plot
  • Diagnostic plots

Week 4

Bootstrapping in R, Permutation Tests

  • An introduction to bootstrapping
  • The sample() function
  • A bootstrap sample
  • Forming several bootstrap samples
    • Aside for loops vs. matrices and speed
      • Using the bootstrap
      • An introduction to permuation tests
      • A permutation test simulation


The statistics prerequisites are noted here because this is a “Learn R to do statistics” course which assumes you are somewhat familiar with basic statistics. This is not a “Learn statistics using your R skills” course. Students should be familiar with introductory statistics before enrolling.

Private: Statistics 1 – Probability and Study Design

This course, the first of a three-course sequence, provides an introduction to statistics for those with little or no prior exposure to basic probability and statistics.
  • Skill: Intermediate
  • Credit Options: CAP, CEU

Private: Statistics 2 – Inference and Association

This course, the second of a three-course sequence, will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.
  • Skill: Intermediate
  • Credit Options: CAP, CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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R for Statistical Analysis

Additional Information


Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

In addition to assigned readings, this course also has practice exercises, supplemental readings available online, and an end-of-class project.

Course Text

The course text is Using R for Introductory Statistics by John Verzani.


You must have the program R installed for the course.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

R for Statistical Analysis