Data Analysis for Chemistry An Introductory Guide for Students and Laboratory Scientists

by Hibbert, D. Brynn; Gooding, J. Justin

• ISBN13: 9780195162103
• ISBN10: 0195162102
• Format: Hardcover
• Copyright: 2005-10-27
• Publisher: Oxford University Press

Summary

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 waysof 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 compromisebetween 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 tofind the information they are seeking to perform the data analysis they think they need.

Table of Contents

Readers' Guide: Definitions, Questions, and Useful Functions: Where to Find Things and What to Do	1	(20)
1. Introduction	21	(18)
1.1. What This Chapter Should Teach You	21	(1)
1.2. Measurement	21	(1)
1.3. Why Measure?	21	(1)
1.4. Definitions	22	(1)
1.5. Calibration and Traceability	23	(1)
1.6. So Why Do We Need to Do Data Analysis at All?	23	(1)
1.7. Three Types of Error	24	(7)
1.8. Accuracy and Precision	31	(4)
1.9. Significant Figures	35	(2)
1.10. Fit for Purpose	37	(2)
2. Describing Data: Means and Confidence Intervals	39	(28)
2.1. What This Chapter Should Teach You	39	(1)
2.2. The Analytical Result	39	(1)
2.3. Population and Sample	40	(1)
2.4. Mean, Variance, and Standard Deviation	41	(8)
2.5. So How Do I Quote My Uncertainty?	49	(12)
2.6. Robust Estimators	61	(3)
2.7. Repeatability and Reproducibility of Measurements	64	(3)
3. Hypothesis Testing	67	(32)
3.1. What This Chapter Should Teach You	67	(1)
3.2. Why Perform Hypothesis Tests?	67	(1)
3.3. Levels of Confidence and Significance	68	(4)
3.4. How to Test If Your Data Are Normally Distributed	72	(5)
3.5. Test for an Outlier	77	(5)
3.6. Determining Significant Systematic Error	82	(5)
3.7. Testing Variances: Are Two Variances Equivalent?	87	(3)
3.8. Testing Two Means (Means t-Test)	90	(4)
3.9. Paired t-Test	94	(3)
3.10. Hypothesis Testing in Excel	97	(2)
4. Analysis of Variance	99	(28)
4.1. What This Chapter Should Teach You	99	(1)
4.2. What Is Analysis of Variance (ANOVA)?	99	(2)
4.3. Jargon	101	(1)
4.4. One-Way ANOVA	101	(4)
4.5. Least Significant Difference	105	(1)
4.6. ANOVA in Excel	106	(6)
4.7. Sampling	112	(3)
4.8. Multiway ANOVA	115	(1)
4.9. Two-Way ANOVA in Excel	116	(9)
4.10. Calculations of Multiway ANOVA	125	(1)
4.11. Variances in Multiway ANOVA	125	(2)
5. Calibration	127	(38)
5.1. What This Chapter Should Teach You	127	(1)
5.2. Introduction	127	(2)
5.3. Linea: Calibration Models	129	(18)
5.4. Calibration in Excel	147	(6)
5.5. r²: A Much Abused Statistic	153	(1)
5.6. The Well-Tempered Calibration	154	(1)
5.7. Standard Addition	155	(5)
5.8. Limits of Detection and Determination	160	(5)
Appendix	165	(4)
Bibliography	169	(4)
Index	173