R for Statistical Analysis

R for Statistical Analysis

taught by John Verzani

 
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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 bootstrapping
  • 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

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:
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.
Organization of the Course:
Options for Credit and Recognition:
Course Text:

The course text is Using R for Introductory Statistics by John Verzani.

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.
Instructor(s):

Dates:

To be scheduled.

R for Statistical Analysis

Instructor(s):

Dates:
To be scheduled.

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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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.

The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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