Introduction to R - Statistical Analysis

Taught by John Verzani

Brief Description:

This course covers how to use R for basic statistical procedures.

Instructor(s):
Level: intermediate

Who Should Take This Course:

Anyone who wants to gain a familiarity with R to facilitate its use in more advanced courses. Also, teachers who wish to use R in teaching introductory statistics.

Dates:
July 27, 2012 to August 24, 2012October 26, 2012 to November 23, 2012
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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Introduction to R - Statistical Analysis

Taught by John Verzani

Aim of Course:

This is a course to "Learn R via your existing knowledge of basic statistics" and 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)"Introduction to R - Data Handling," for an introduction to programming in R.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

• Using R

Prerequisite(s):
Familiarity with R is not assumed. The prerequisites are noted 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.

• Introduction to Statistics for Beginners
• Introduction to Statistics 1: Sampling and Inference

Course Program:

SESSION 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

SESSION 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

SESSION 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

SESSION 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.

Organization of the Course:

This course takes place over the internet 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.

The course typically requires 15 hours per week. 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.

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, which you can order from CRC Press, or by using this form. CRC Press typically gives students a generous discount when students order the text using the above form (not by ordering the text online).

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

You must have a copy of R for the course. Click Here for information on obtaining a free copy.

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Introduction to R - Statistical Analysis

Taught by John Verzani

Instructor(s):
Dates:
July 27, 2012 to August 24, 2012October 26, 2012 to November 23, 2012
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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