A Practical Guide to Scientific Data Analysis

David N. Livingstone

Die Leseprobe wird geladen.
Buch (gebundene Ausgabe, Englisch)
Buch (gebundene Ausgabe, Englisch)
Fr. 129.00
Fr. 129.00
inkl. gesetzl. MwSt.
inkl. gesetzl. MwSt.
Versandfertig innert 6 - 9 Werktagen Versandkostenfrei
Versandfertig innert 6 - 9 Werktagen
Versandkostenfrei

Weitere Formate

gebundene Ausgabe

Fr. 129.00

Accordion öffnen
  • A Practical Guide to Scientific Data Analysis

    John Wiley & Sons

    Versandfertig innert 6 - 9 Werktagen

    Fr. 129.00

    John Wiley & Sons

eBook (PDF)

Fr. 70.00

Accordion öffnen

Beschreibung

A practical handbook aimed at the working scientist, it covers the application of statistical and mathematical methods to the design of "performance" chemicals, such as pharmaceuticals, agrochemicals, fragrances, flavours and paints. This volume will have wide appeal, not only to chemists, but biochemists, pharmacists and other researchers within the field of statistical analysis of experimental results.
* The first book in this field to address this topic
* The statistics book for the non-statistician
* Highly qualified and internationally respected author

Produktdetails

Einband gebundene Ausgabe
Seitenzahl 358
Erscheinungsdatum 01.01.2010
Sprache Englisch
ISBN 978-0-470-85153-1
Verlag John Wiley & Sons, Inc.
Maße (L/B/H) 23.7/16.2/2.9 cm
Gewicht 642 g
Auflage 1. Auflage

Kundenbewertungen

Es wurden noch keine Bewertungen geschrieben.
  • Artikelbild-0
  • Preface

    Abbreviations

    Chapter 1 Introduction: Data and it's Properties, Analytical Methods and Jargon

    1.1 Introduction

    1.2 Types of Data

    1.3 Sources of Data

    1.4 The nature of data

    1.5 Analytical methods

    References

    Chapter 2 Experimental Design - Experiment and Set Selection

    2.1 What is Experimental Design?

    2.2 Experimental Design Techniques

    2.3 Strategies for Compound Selection

    2.4 High Throughput Experiments

    2.5 Summary

    References

    Chapter 3 Data Pre-treatment and Variable Selection

    3.1 Introduction

    3.2 Data Distribution

    3.3 Scaling

    3.4 Correlations

    3.5 Data Reduction

    3.6 Variable Selection

    3.7 Summary

    References

    Chapter 4 Data Display

    4.1 Introduction

    4.2 Linear Methods

    4.3 Non-linear Methods

    4.4 Faces, Flowerplots & Friends

    4.5 Summary

    References

    Chapter 5 Unsupervised Learning

    5.1 Introduction

    5.2 Nearest-neighbour Methods

    5.3 Factor Analysis

    5.4 Cluster Analysis

    5.5 Cluster Significance Analysis

    5.6 Summary

    References

    Chapter 6 Regression analysis

    6.1 Introduction

    6.2 Simple Linear Regression

    6.3 Multiple Linear Regression

    6.4 Multiple regression - Robustness, Chance Effects, the Comparison of Models and Selection Bias

    6.5 Summary

    References

    Chapter 7 Supervised Learning

    7.1 Introduction

    7.2 Discriminant Techniques

    7.3 Regression on principal Components & PLS

    7.4 Feature Selection.

    7.5 Summary

    References

    Chapter 8 Multivariate dependent data

    8.1 Introduction

    8.2 Principal Components and Factor Analysis

    8.3 Cluster Analysis

    8.4 Spectral Map Analysis

    8.5 Models with Multivariate Dependent and Independent Data

    8.6 Summary

    References

    Chapter 9 Artificial Intelligence & Friends

    9.1 introduction

    9.2 Expert Systems

    9.3 Neural Networks

    9.4 Miscellaneous AI techniques

    9.5 Genetic Methods

    9.6 Consensus Models

    9.7 Summary

    References

    Chapter 10 Molecular Design

    10.1 The Need for Molecular Design

    10.2 What is QSAR/QSPR?

    10.3 Why Look for Quantitative Relationships?

    10.4 Modelling Chemistry

    10.5 Molecular Field and Surfaces

    10.6 Mixtures

    10.7 Summary

    References