h264, yuv420p, 1280x720 |ENGLISH, 44100 Hz, 2 Channels | 11h 10mn | 6.37 GB

An introductory course that teaches the foundations of scientific and statistical programming using R software.

What you'll learn

Understand how to create and manipulate R data structures used in scientific programming applications.

Understand and use important statistical R programming concepts such as looping and control structures, interactive data input and formatting output, writing functions as programs, writing output to a file and plotting output.

Understand and be able to use the R apply family of functions efficiently.

Know how to debug programs and how to make programs run more efficiently.

Understand and be able to implement various resampling methods effectively, including bootstrapping, jackknifing and N-fold cross validation.

Requirements

Students will need to install the popular no-cost R Console and RStudio software (instructions provided).

Description

Programming Statistical Applications in R is an introductory course teaching the basics of programming mathematical and statistical applications using the R language. The course makes extensive use of the Introduction to Scientific Programming and Simulation using R (spuRs) package from the Comprehensive R Archive Network (CRAN). The course is a scientific-programming foundations course and is a useful complement and precursor to the more simulation-application oriented R Programming for Simulation and Monte-Carlo Methods Udemy course. The two courses were originally developed as a two-course sequence (although they do share some exercises in common). Together, both courses provide a powerful set of unique and useful instruction about how to create your own mathematical and statistical functions and applications using R software.

Programming Statistical Applications in R is a "hands-on" course that comprehensively teaches fundamental R programming skills, concepts and techniques useful for developing statistical applications with R software. The course also uses dozens of "real-world" scientific function examples. It is not necessary for a student to be familiar with R, nor is it necessar



DOWNLOAD
uploadgig