Data Analysis for Chemistry An Introductory

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.

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. Linear 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

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

Summary

Table of Contents

Supplemental Materials