25 Recipes for Getting Started with R

Autorentext
Paul Teetor is a quantitative developer with Masters degrees in statistics and computer science. He specializes in analytics and software engineering for investment management, securities trading, and risk management. He works with hedge funds, market makers, and portfolio managers in the greater Chicago area.

Klappentext
R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression.

You'll find recipes on reading data files, creating data frames, computing basic statistics, testing means and correlations, creating a scatter plot, performing simple linear regression, and many more. These solutions were selected from O'Reilly's R Cookbook, which contains more than 200 recipes for R that you'll find useful once you move beyond the basics.

Zusammenfassung
This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to get started with R for basic statistics, graphics, and regression.

Inhalt
Preface; Other Resources; Downloading Additional Packages; Software and Platform Notes; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Chapter 1: The Recipes; 1.1 Downloading and Installing R; 1.2 Getting Help on a Function; 1.3 Viewing the Supplied Documentation; 1.4 Searching the Web for Help; 1.5 Reading Tabular Datafiles; 1.6 Reading from CSV Files; 1.7 Creating a Vector; 1.8 Computing Basic Statistics; 1.9 Initializing a Data Frame from Column Data; 1.10 Selecting Data Frame Columns by Position; 1.11 Selecting Data Frame Columns by Name; 1.12 Forming a Confidence Interval for a Mean; 1.13 Forming a Confidence Interval for a Proportion; 1.14 Comparing the Means of Two Samples; 1.15 Testing a Correlation for Significance; 1.16 Creating a Scatter Plot; 1.17 Creating a Bar Chart; 1.18 Creating a Box Plot; 1.19 Creating a Histogram; 1.20 Performing Simple Linear Regression; 1.21 Performing Multiple Linear Regression; 1.22 Getting Regression Statistics; 1.23 Diagnosing a Linear Regression; 1.24 Predicting New Values; 1.25 Accessing the Functions in a Package;

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