R for Statistical Analysis

# R for Statistical Analysistaught by John Verzani

Aim of Course:

In this online course, “R Statistics,” you will "Learn R via your existing knowledge of basic statistics". "R Statistics" does not treat statistical concepts in depth. 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 related course (right) "R Programming - Introduction 1," for an introduction to programming in R.

Course Program:

## 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 independed samples no equal variance assumption
• Two independed 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 regression
• 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

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.

# R for Statistical Analysis

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.
Level:
Intermediate
Prerequisite:
Note:  The statistics prerequisites are noted here because this is a "Learn R to do statistics (with which you are somewhat familiar)" course, not a "Learn statistics using your R skills" course.
You should be familiar with introductory statistics.  Try these self tests to check your knowledge.
Organization of the 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 Requirement:

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
4. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses

INFORMS CAP:
This course is also 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 .
Course Text:

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

Software:
You must have a copy of R for the course. Click Here for information on obtaining a free copy.
Instructor(s):

Dates:

March 15, 2019 to April 12, 2019 September 27, 2019 to October 25, 2019 March 13, 2020 to April 10, 2020 September 25, 2020 to October 23, 2020

# R for Statistical Analysis

Instructor(s):

Dates:
March 15, 2019 to April 12, 2019 September 27, 2019 to October 25, 2019 March 13, 2020 to April 10, 2020 September 25, 2020 to October 23, 2020

Course Fee: \$549

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).