Categories

- Author: Jared P. Lander
- Pages: 464
- Language: English
- ISBN/ASIN: 321888030
- ISBN13: 9780321888037
- Upload date: 30-12-2016, 16:16
- Category: Mathematics

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.

Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.

Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R navigate and use the R environment master basic program control, data import, and manipulation and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques.

By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most.

COVERAGE INCLUDES

• Exploring R, RStudio, and R packages

• Using R for math: variable types, vectors, calling functions, and more

• Exploiting data structures, including data.frames, matrices, and lists

• Creating attractive, intuitive statistical graphics

• Writing user-defined functions

• Controlling program flow with if, ifelse, and complex checks

• Improving program efficiency with group manipulations

• Combining and reshaping multiple datasets

• Manipulating strings using R’s facilities and regular expressions

• Creating normal, binomial, and Poisson probability distributions

• Programming basic statistics: mean, standard deviation, and t-tests

• Building linear, generalized linear, and nonlinear models

• Assessing the quality of models and variable selection

• Preventing overfitting, using the Elastic Net and Bayesian methods

• Analyzing univariate and multivariate time series data

• Grouping data via K-means and hierarchical clustering

• Preparing reports, slideshows, and web pages with knitr

• Building reusable R packages with devtools and Rcpp

• Getting involved with the R global community

- Download R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics Series) - UseNet cloud
- http://k2s.cc/file/52c70d3d616ad

* If download links doesn't work. Please write a comment.

R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics Series) Download via usenet
Using R for Data Management, Statistical Analysis, and Graphics Simulating Data with SAS SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition R For Dummies, 2 edition Introductory Statistics with R (Statistics and Computing) Instant R Starter Foundations of Linear and Generalized Linear Models Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) Predictive Analytics For Dummies Statistical Methods for Astronomical Data Analysis Agricultural Statistical Data Analysis Using Stata Think Stats, 2 edition Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics (Use R!) R in Action: Data Analysis and Graphics with R R Cookbook (O'Reilly Cookbooks) Practical Business Analytics Using SAS: A Hands-on Guide Learning R Learning Predictive Analytics with Python Hands-On Programming with R: Write Your Own Functions and Simulations Discovering Knowledge in data: An Introduction to Data Mining (Wiley Series on Methods and Applications in Data Mining) C Programming Absolute Beginner's Guide (3rd Edition) Agile Data Science: Building Data Analytics Applications with Hadoop

Debra Webb | Vente Libro | White | Rebecca Raisin | Kamal Ravikant | Ronald Mcrae | Frank Flake Leitfaden Rettungsdienst | Bioconjugate | Castings | Mebane Faber | Jemma Thorne | Practical Aspects | Concentration | Hochspannungsmesstechnik | Bwl Für Dummies | Jürgen Weber | Massive Results | Emery | Dixie | Elizabeth Kingston | Gloria | Jade Remedies | Johnathan Bishop | Delusion Deception | Caribbean Food | Caribbean Recipes | Caribbean Cuisine | Jowett | Error | Error Quadrature | Tobias K. Dobler Leitfaden Osteopathie | Antivirus | Virus | Black Edge | Technical Analysis Profiting |