R for Statistical Analysis

R for Statistical Analysis

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

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

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Overview

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.

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.

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 superprocesses. Dr. Hayden has been teaching statistics since 1982, and is a recognized authority on the teaching of introductory statistics. He has published articles on this subject in The American Statistician, the MAA Notes series, and elsewhere. He has received a national award from high school statistics teachers for his online guidance to teachers of Advanced Placement Statistics, something he has been doing since 1996. Dr. Hayden has also had careers in mechanical engineering and mathemat...
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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

Class Dates

2019

No classes scheduled at this time.

2020

No classes scheduled at this time.

2021

No classes scheduled at this time.

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Prerequisites

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.

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

Recommended

We recommended, but do not require as eligibility to enroll in this course, an understanding of the material covered in these following courses.

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.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CAP, CEU

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.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CEU

What Our Students Say​

This was a great introduction to Programming in R. I feel like I have some basic concepts down and I am looking forward to taking more courses to keep developing my skills in this programming language. I can definitely see the potential for data analysis in my work!

Peter Weddel
Stratus Ag Research

I really appreciated the instructors' contributions to the discussion — their support, their patience, and their feedback.

C. Engel
Stanford University

Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

R Programming – Introduction Part 1

This course provides an easy introduction to programming in R.
Topic: Data Science, Using R | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 10, 2020, May 15, 2020, Sep 11, 2020

R Programming – Introduction Part 2

This course is a continuation of the introduction to R programming.
Topic: Data Science, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 13, 2020, Jul 17, 2020, Nov 13, 2020, Mar 12, 2021

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

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.

Please order a copy of your course textbook prior to course start date.

Software

You must have the program R installed for the course.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

 

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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R for Statistical Analysis
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