By Rick Wicklin
Information simulation is a primary approach in statistical programming and learn. Rick Wicklin's Simulating info with SAS brings jointly the main helpful algorithms and the easiest programming ideas for effective info simulation in an obtainable how-to e-book for working towards statisticians and statistical programmers.
This booklet discusses intimately the right way to simulate facts from universal univariate and multivariate distributions, and the way to take advantage of simulation to judge statistical suggestions. It additionally covers simulating correlated facts, information for regression versions, spatial information, and information with given moments. It presents assistance and strategies for starting programmers, and provides libraries of capabilities for complicated practitioners.
because the first publication dedicated to simulating info throughout quite a number statistical functions, Simulating info with SAS is a necessary instrument for programmers, analysts, researchers, and scholars who use SAS software program.
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Additional resources for Simulating Data with SAS
1 Finding the Names of ODS Tables . . . . . . 2 Selecting and Excluding ODS Tables . . . . . 3 Creating Data Sets from ODS Tables . . . . . 4 Creating ODS Statistical Graphics . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 30 30 31 32 32 33 33 34 35 35 37 40 41 44 44 45 45 46 47 What Every Programmer Should Know before Simulating Data This chapter describes basic statistical distributional theory and details of SAS software that are essential to know before you begin simulating data.
Simulating Data with SAS®. , Cary, North Carolina, USA. ALL RIGHTS RESERVED. com/bookstore. 2 Monte Carlo Estimates The previous section describes the essential ideas of statistical simulation without using mathematics. This section uses statistical theory to describe the essential ideas. The presentation and notation are based on Ross (2006, p. 117–126), which is an excellent reference for readers who are interested in a more rigorous treatment of simulation than is presented in this book. One use of simulation is to estimate some unknown quantity, Â , in the population by some statistic, X, whose expected value is Â.
Cary, North Carolina, USA. ALL RIGHTS RESERVED. com/bookstore. 34 Chapter 3: Preliminary and Background Information data a; call streaminit(4321); do i = 1 to 10; x=rand("uniform"); output; run; data b; call streaminit(4321); do i = 1 to 10; x=rand("uniform"); output; run; proc compare base=a compare=b; run; end; end; /* show they are identical */ This advice also applies to writing a macro function that generates random numbers. Do not explicitly set a seed value inside the macro. If you do, then each run of the macro will generate the same values.