# R for Statistical Analysis

Instructor(s):

Dates:

April 03, 2015 to May 01, 2015 July 24, 2015 to August 21, 2015 October 02, 2015 to October 30, 2015 January 01, 2016 to January 29, 2016 April 01, 2016 to April 29, 2016 July 22, 2016 to August 19, 2016 October 07, 2016 to November 04, 2016 January 06, 2017 to February 03, 2017 March 31, 2017 to April 28, 2017 July 21, 2017 to August 18, 2017 October 06, 2017 to November 03, 2017

# 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 boostrapping
• 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

Instructor(s):

Dates:
April 03, 2015 to May 01, 2015 July 24, 2015 to August 21, 2015 October 02, 2015 to October 30, 2015 January 01, 2016 to January 29, 2016 April 01, 2016 to April 29, 2016 July 22, 2016 to August 19, 2016 October 07, 2016 to November 04, 2016 January 06, 2017 to February 03, 2017 March 31, 2017 to April 28, 2017 July 21, 2017 to August 18, 2017 October 06, 2017 to November 03, 2017

Course Fee: \$549

Tuition Savings:  When you register online for 3 or more courses, \$200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

Register Now

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. Please use this printed registration form, for these and other special orders.

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.

# R for Statistical Analysistaught by John Verzani

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:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.
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.

If you are unclear as to whether you have mastered the above requirements, try these placement tests.

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: about 15 hours per week, at times of  your choosing.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. 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. 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, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

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.

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