Introduction to R – Statistical Analysis

Now 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. This is covered in Dr. John Verzani online course “Introduction to R – Statistical Analysis” at statistics.com. For more detail please visit at http://www.statistics.com/Rstatistics.

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:

Course outline: The course is structured as follows

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

Instructor, 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.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at www.india.statistics.com.

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com