Data Analysis for Chemistry An Introductory Guide for Students and Laboratory Scientists 1st Edition by Brynn Hibbert, Justin Gooding 0195162110 9780195162110

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ISBN 10: 0195162110

ISBN 13: 9780195162110

Author: D. Brynn Hibbert; J. Justin Gooding

Chemical data analysis, with aspects of metrology in chemistry and chemometrics, is an evolving discipline where new and better ways of doing things are constantly being developed. This book makes data analysis simple by demystifying the language and whenever possible giving unambiguous ways of doing things. Based on author D. Brynn Hibberts lectures on data analysis to undergraduates and graduate students, Data Analysis for Chemistry covers topics including measurements, means and confidence intervals, hypothesis testing, analysis of variance, and calibration models. The end result is a compromise between recipes of how to perform different aspects of data analysis, and basic information on the background principles behind the recipes to be performed. An entry level book targeted at learning and teaching undergraduate data analysis, Data Analysis for Chemistry makes it easy for readers to find the information they are seeking to perform the data analysis they think they need.

Data Analysis for Chemistry An Introductory Guide for Students and Laboratory Scientists 1st Table of contents:

1. Introduction

1.1. What This Chapter Should Teach You

1.2. Measurement

1.3. Why Measure?

1.4. Definitions

1.5. Calibration and Traceability

1.6. So Why Do We Need to Do Data Analysis at All?

1.7. Three Types of Error

1.8. Accuracy and Precision

1.9. Significant Figures

1.10. Fit for Purpose

2. Describing Data: Means and Confidence Intervals

2.1. What This Chapter Should Teach You

2.2. The Analytical Result

2.3. Population and Sample

2.4. Mean, Variance, and Standard Deviation

2.5. So How Do I Quote My Uncertainty?

2.6. Robust Estimators

2.7. Repeatability and Reproducibility of Measurements

3. Hypothesis Testing

3.1. What This Chapter Should Teach You

3.2. Why Perform Hypothesis Tests?

3.3. Levels of Confidence and Significance

3.4. How to Test If Your Data Are Normally Distributed

3.5. Test for an Outlier

3.6. Determining Significant Systematic Error

3.7. Testing Variances: Are Two Variances Equivalent?

3.8. Testing Two Means (Means t-Test)

3.9. Paired t-Test

3.10. Hypothesis Testing in Excel

4. Analysis of Variance

4.1. What This Chapter Should Teach You

4.2. What Is Analysis of Variance (ANOVA)?

4.3. Jargon

4.4. One-Way ANOVA

4.5. Least Significant Difference

4.6. ANOVA in Excel

4.7. Sampling

4.8. Multiway ANOVA

4.9. Two-Way ANOVA in Excel

4.10. Calculations of Multiway ANOVA

4.11. Variances in Multiway ANOVA

5. Calibration

5.1. What This Chapter Should Teach You

5.2. Introduction

5.3. Linear Calibration Models

5.4. Calibration in Excel

5.5. r2: A Much Abused Statistic

5.6. The Well-Tempered Calibration

5.7. Standard Addition

5.8. Limits of Detection and Determination

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