Richard A Armstrong, Anthony C Hilton

Statistical Analysis in Microbiology

Statnotes

• Broschiertes Buch

Produktbeschreibung

This book is aimed primarily at microbiologists who are undertaking research, and who require a basic knowledge of statistics to analyse their experimental data. Computer software employing a wide range of data analysis methods is widely available to experimental scientists. The availability of this software, however, makes it even more essential that microbiologists understand the basic principles of statistics. Statistical analysis of data can be complex with many different methods of approach, each of which applies in a particular experimental circumstance. In addition, most statistical software commercially available is complex and difficult to use. Hence, it is easy to apply an incorrect statistical method to data and to draw the wrong conclusions from an experiment. The purpose of this book is an attempt to present the basic logic of statistics as clearly as possible and therefore, to dispel some of the myths that often surround the subject. The book is presented as a series of 2018Statnotes', many of which were originally published in the 2018Microbiologist' by the Society for Applied Microbiology, each of which deals with various topics including the nature of variables, comparing the means of two or more groups, non-parametric statistics, analysis of variance, correlating variables, and more complex methods such as multiple linear regression and factor analysis. In each case, the relevant statistical methods are illustrated with scenarios and real experimental data drawn from experiments in microbiology. The text will incorporate a glossary of the most commonly used statistical terms and a section to aid the investigator to select the most appropriate test.

Produktdetails

• Verlag: John Wiley & Sons / Wiley
• Seitenzahl: 192
• Erscheinungstermin: 14. Dezember 2010
• Englisch
• Abmessung: 254mm x 178mm x 11mm
• Gewicht: 373g
• ISBN-13: 9780470559307
• ISBN-10: 0470559306
• Artikelnr.: 32037003

Autorenporträt

Dr. Richard Armstrong is a?Lecturer for the School of Life and Health Sciences for Aston University. His research interests include?Neuropathology of Alzheimer's Disease; Lichen Biology and Ecology. He has?published in many?peer reviewed journals. Dr. Anthony C. Hilton is a Senior Lecturer in Microbiology and the UG Programme Director at Aston University in the School of Life and Health Sciences. His research interests include applied microbiology (food and clinical), molecular microbiology, salmonella, campylobacter, MRSA and Escherichia?coli 0157, clostridium difficult, and molecular typing of microorganisms.?In addition to contributing to various peer reviewed journals, he is also member of the Molecular Biosciences Research Group.

Inhaltsangabe

Preface. Acknowledgments. Note on Statistical Software. 1 ARE THE DATA
NORMALLY DISTRIBUTED? 1.1 Introduction. 1.2 Types of Data and Scores. 1.3
Scenario. 1.4 Data. 1.5 Analysis: Fitting the Normal Distribution. 1.6
Conclusion. 2 DESCRIBING THE NORMAL DISTRIBUTION. 2.1 Introduction. 2.2
Scenario. 2.3 Data. 2.4 Analysis: Describing the Normal Distribution. 2.5
Analysis: Is a Single Observation Typical of the Population? 2.6 Analysis:
Describing the Variation of Sample Means. 2.7 Analysis: How to Fit
Confidence Intervals to a Sample Mean. 2.8 Conclusion. 3 TESTING THE
DIFFERENCE BETWEEN TWO GROUPS. 3.1 Introduction. 3.2 Scenario. 3.3 Data.
3.4 Analysis: The Unpaired t Test. 3.5 One-Tail and Two-Tail Tests. 3.6
Analysis: The Paired t Test. 3.7 Unpaired versus the Paired Design. 3.8
Conclusion. 4 WHAT IF THE DATA ARE NOT NORMALLY DISTRIBUTED? 4.1
Introduction. 4.2 How to Recognize a Normal Distribution. 4.3 Nonnormal
Distributions. 4.4 Data Transformation. 4.5 Scenario. 4.6 Data. 4.7
Analysis: Mann-Whitney U test (for Unpaired Data). 4.8 Analysis: Wilcoxon
Signed-Rank Test (for Paired Data). 4.9 Comparison of Parametric and
Nonparametric Tests. 4.10 Conclusion. 5 CHI-SQUARE CONTINGENCY TABLES. 5.1
Introduction. 5.2 Scenario. 5.3 Data. 5.4 Analysis: 2 x 2 Contingency
Table. 5.5 Analysis: Fisher's 2 x 2 Exact Test. 5.6 Analysis: Rows x
Columns (R x C) Contingency Tables. 5.7 Conclusion. 6 ONE-WAY ANALYSIS OF
VARIANCE (ANOVA). 6.1 Introduction. 6.2 Scenario. 6.3 Data. 6.4 Analysis.
6.5 Assumptions of ANOVA. 6.6 Conclusion. 7 POST HOC TESTS. 7.1
Introduction. 7.2 Scenario. 7.3 Data. 7.4 Analysis: Planned Comparisons
between the Means. 7.5 Analysis: Post Hoc Tests. 7.6 Conclusion. 8 IS ONE
SET OF DATA MORE VARIABLE THAN ANOTHER? 8.1 Introduction. 8.2 Scenario. 8.3
Data. 8.4 Analysis of Two Groups: Variance Ratio Test. 8.5 Analysis of
Three or More Groups: Bartlett's Test. 8.6 Analysis of Three or More
Groups: Levene's Test. 8.7 Analysis of Three or More Groups: Brown-Forsythe
Test. 8.8 Conclusion. 9 STATISTICAL POWER AND SAMPLE SIZE. 9.1
Introduction. 9.2 Calculate Sample Size for Comparing Two Independent
Treatments. 9.3 Implications of Sample Size Calculations. 9.4 Calculation
of the Power (P') of a Test. 9.5 Power and Sample Size in Other Designs.
9.6 Power and Sample Size in ANOVA. 9.7 More Complex Experimental Designs.
9.8 Simple Rule of Thumb. 9.9 Conclusion. 10 ONE-WAY ANALYSIS OF VARIANCE
(RANDOM EFFECTS MODEL): THE NESTED OR HIERARCHICAL DESIGN. 10.1
Introduction. 10.2 Scenario. 10.3 Data. 10.4 Analysis. 10.5 Distinguish
Random- and Fixed-Effect Factors. 10.6 Conclusion. 11 TWO-WAY ANALYSIS OF
VARIANCE. 11.1 Introduction. 11.2 Scenario. 11.3 Data. 11.4 Analysis. 11.5
Conclusion. 12 TWO-FACTOR ANALYSIS OF VARIANCE. 12.1 Introduction. 12.2
Scenario. 12.3 Data. 12.4 Analysis. 12.5 Conclusion. 13 SPLIT-PLOT ANALYSIS
OF VARIANCE. 13.1 Introduction. 13.2 Scenario. 13.3 Data. 13.4 Analysis.
13.5 Conclusion. 14 REPEATED-MEASURES ANALYSIS OF VARIANCE. 14.1
Introduction. 14.2 Scenario. 14.3 Data. 14.4 Analysis. 14.5 Conclusion. 15
CORRELATION OF TWO VARIABLES. 15.1 Introduction. 15.2 Naming Variables.
15.3 Scenario. 15.4 Data. 15.5 Analysis. 15.6 Limitations of r. 15.7
Conclusion. 16 LIMITS OF AGREEMENT. 16.1 Introduction. 16.2 Scenario. 16.3
Data. 16.4 Analysis. 16.5 Conclusion. 17 NONPARAMETRIC CORRELATION
COEFFICIENTS. 17.1 Introduction. 17.2 Bivariate Normal Distribution. 17.3
Scenario. 17.4 Data. 17.5 Analysis: Spearman's Rank Correlation (Á, rs).
17.6 Analysis: Kendall's Rank Correlation (Ä). 17.7 Analysis: Gamma (³).
17.8 Conclusion. 18 FITTING A REGRESSION LINE TO DATA. 18.1 Introduction.
18.2 Line of Best Fit. 18.3 Scenario. 18.4 Data. 18.5 Analysis: Fitting the
Line. 18.6 Analysis: Goodness of Fit of the Line to the Points. 18.7
Conclusion. 19 USING A REGRESSION LINE FOR PREDICTION AND CALIBRATION. 19.1
Introduction. 19.2 Types of Prediction Problem. 19.3 Scenario. 19.4 Data.
19.5 Analysis. 19.6 Conclusion. 20 COMPARISON OF REGRESSION LINES. 20.1
Introduction. 20.2 Scenario. 20.3 Data. 20.4 Analysis. 20.5 Conclusion. 21
NONLINEAR REGRESSION: FITTING AN EXPONENTIAL CURVE. 21.1 Introduction. 21.2
Common Types of Curve. 21.3 Scenario. 21.4 Data. 21.5 Analysis. 21.6
Conclusion. 22 NONLINEAR REGRESSION: FITTING A GENERAL POLYNOMIAL-TYPE
CURVE. 22.1 Introduction. 22.2 Scenario A: Does a Curve Fit Better Than a
Straight Line? 22.3 Data. 22.4 Analysis. 22.5 Scenario B: Fitting a General
Polynomial-Type Curve. 22.6 Data. 22.7 Analysis. 22.8 Conclusion. 23
NONLINEAR REGRESSION: FITTING A LOGISTIC GROWTH CURVE. 23.1 Introduction.
23.2 Scenario. 23.3 Data. 23.4 Analysis: Nonlinear Estimation Methods. 23.6
Conclusion. 24 NONPARAMETRIC ANALYSIS OF VARIANCE. 24.1 Introduction. 24.2
Scenario. 24.3 Analysis: Kruskal-Wallis Test. 24.4 Analysis: Friedmann's
Test. 24.5 Conclusion. 25 MULTIPLE LINEAR REGRESSION. 25.1 Introduction.
25.2 Scenario. 25.3 Data. 25.4 Analysis. 25.5 Conclusion. 26 STEPWISE
MULTIPLE REGRESSION. 26.1 Introduction. 26.2 Scenario. 26.3 Data. 22.4
Analysis by the Step-Up Method. 26.5 Conclusion. 27 CLASSIFICATION AND
DENDROGRAMS. 27.1 Introduction. 27.2 Scenario. 27.3 Data. 27.4 Analysis.
27.5 Conclusion. 28 FACTOR ANALYSIS AND PRINCIPAL COMPONENTS ANALYSIS. 28.1
Introduction. 28.2 Scenario. 28.3 Data. 28.4 Analysis: Theory. 28.5
Analysis: How Is the Analysis Carried Out? 28.6 Conclusion. References.
Appendix 1 Which Test to Use: Table. Appendix 2 Which Test to Use: Key.
Appendix 3 Glossary of Statistical Terms and Their Abbreviations. Appendix
4 Summary of Sample Size Procedures for Different Statistical Tests. Index
of Statistical Tests and Procedures.