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.**

Call:
020 66009116

Websites:

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