Uncertainty analysis of experimental data with R 1st Edition Benjamin David 1315342596 9781315342597

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Product details:

• ISBN 10: 1315342596
• ISBN 13: 9781315342597
• Author: David Shaw

“This would be an excellent book for undergraduate, graduate and beyond….The style of writing is easy to read and the author does a good job of adding humor in places. The integration of basic programming in R with the data that is collected for any experiment provides a powerful platform for analysis of data…. having the understanding of data analysis that this book offers will really help researchers examine their data and consider its value from multiple perspectives – and this applies to people who have small AND large data sets alike! This book also helps people use a free and basic software system for processing and plotting simple to complex functions.” Michelle Pantoya, Texas Tech University Measurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas, cannot be measured exactly and there will always be some uncertainty with these measured values, so it is vital for researchers to be able to quantify this data. Uncertainty Analysis of Experimental Data with R covers methods for evaluation of uncertainties in experimental data, as well as predictions made using these data, with implementation in R. The books discusses both basic and more complex methods including linear regression, nonlinear regression, and kernel smoothing curve fits, as well as Taylor Series, Monte Carlo and Bayesian approaches.

Table of contents:

1. Introduction

2. Aspects of R

3. Statistics

4. Curve Fits

5. Uncertainty of a Measured Quantity

6. Uncertainty of a Result Calculated Using Experimental Data

7. Taylor Series Uncertainty of a Linear Regression Curve Fit

8. Monte Carlo Methods

9. The Bayesian Approach

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