Introduction to R - Statistical Analysis
Dr. John VerzaniAim of Course:
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 also the related course "Introduction to R - Data Handling," which provides an introduction to programming in R.
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.Course Program:
The course is structured as followsSESSION 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
- 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
- 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
- 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
The Instructor:
John Verzani is member of the faculty at the College of Staten Island of the City University of New York, and the author of the course text Using R for Introductory Statistics. His research interests and publications are in the area of superprocesses.Organization of the Course:
The course takes place over the internet, at statistics.com. 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 is scheduled to take place over 4 weeks, and typically requires 10-15 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 and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.Certificates and Grades:
You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Professional Advancement Program that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.Credit:
This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.Dates:
Sep. 5 - Oct. 3, 2008Click here to be notified of future course offerings.
Participants gain access to the online materials on the first day of the course, and typically spend about 10-15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.
Level:
introductory/intermediatePrerequisite:
The equivalent of Introduction to Statistics I: Inference for a Single Variable, and Introduction to Statistics II: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners). Familiarity with R is not assumed. The above prerequisites are noted because this is a "Learn R to do statistics (with which you are somewaht familiar)" course, not a "Learn statistics using your R skills" course.Course Text:
The course text is Using R for Introductory Statistics by John Verzani, CRC Press. This text can be ordered directly from CRC press using this form. CRC Press usually gives a 25% discount when the book is ordered using the above form.Software:
You must have a copy of R for the course. Click Here for information on obtaining a free copy.Registration:
Register Online - $449Register Online (academic) - $349 (you must be affiliated with a college, university or high school)
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.
Note: 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.