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Statistics for Biomedical Engineers and Scientists: How to Visualize and Analyze Data [Pehme köide]

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(Reader in Medical Image Analysis, School of Biomedical Engineering and Imaging Science, King's College London.), (Senior Lecturer, Division of Imaging Sciences and Biomedical Engineering, King's College, London, UK)
  • Formaat: Paperback / softback, 274 pages, kõrgus x laius: 235x191 mm, kaal: 560 g
  • Ilmumisaeg: 21-May-2019
  • Kirjastus: Academic Press Inc.(London) Ltd
  • ISBN-10: 008102939X
  • ISBN-13: 9780081029398
Teised raamatud teemal:
Statistics for Biomedical Engineers and Scientists: How to Visualize and Analyze DataSuurem pilt
  • Formaat: Paperback / softback, 274 pages, kõrgus x laius: 235x191 mm, kaal: 560 g
  • Ilmumisaeg: 21-May-2019
  • Kirjastus: Academic Press Inc.(London) Ltd
  • ISBN-10: 008102939X
  • ISBN-13: 9780081029398
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780081029398.html
Märksõnad:

Statistics for Biomedical Engineers and Scientists: How to Analyze and Visualize Data gives an intuitive understanding of the concepts of basic statistics with a focus on solving biomedical problems. It gives the practical skills to use statistics to visualize and analyze data; to ask, and answer, questions about data.

Practical activities and exercises are provided for solving both ‘by hand’ and through using MATLAB. The book could still be used as a book on practical and applied statistics without engaging with the MATLAB exercises.

This text is ideal for students in biomedical engineering and biomedical science taking a course on basic statistics and for researchers who need a self-learning handbook to help them visualize and analyze their data.

This book will enable readers to:

  • Demonstrate an understanding of the fundamental concepts of descriptive and inferential statistics
  • Analyze data and choose an appropriate hypothesis test to answer a given question
  • Compute numerical statistical measures and perform hypothesis tests ‘by hand’
  • Visualize data and perform statistical analysis using MATLAB
  • A practical guide to visualizing and analyzing statistical data
  • Many practical examples and exercises to illustrate the power of statistics in biomedical engineering applications
  • Gives an intuitive understanding of statistical tests
  • Imparts practical skills by showing both how to perform operations ‘by hand’ and use MATLAB as a computational tool
  • Online downloadable materials for students and teachers

Arvustused

"This book is a very interesting introduction to the basic concepts of statistics. The majority of examples are taken from medicine and biology. However, the scope of the book is more general, so it is worth to be read by everybody, who wants to apply statistical methods in any field." --zbMATH

About the Authors xv
Preface xvii
Acknowledgments xxiii
Chapter 1 Descriptive Statistics I: Univariate Statistics
1(22)
1.1 Introduction
1(1)
1.2 Types of Statistical Data
2(2)
1.3 Univariate Data Visualization
4(4)
1.3.1 Dotplot
4(1)
1.3.2 Histogram
5(1)
1.3.3 Bar Chart
6(2)
1.4 Measures of Central Tendency
8(2)
1.4.1 Mean
8(1)
1.4.2 Median
8(1)
1.4.3 Mode
9(1)
1.4.4 Which Measure of Central Tendency to Use?
9(1)
1.5 Measures of Variation
10(2)
1.5.1 Standard Deviation
10(1)
1.5.2 Interquartile Range
10(1)
1.5.3 Which Measure of Variation to Use?
11(1)
1.6 Visualizing Measures of Variation
12(1)
1.6.1 Visualizing Mean and Standard Deviation
12(1)
1.6.2 Visualizing Median and IQR: The Box Plot
12(1)
1.7 Summary
13(1)
1.8 Using MATLAB for Univariate Descriptive Statistics
14(3)
1.8.1 Visualization of Univariate Data
14(1)
1.8.2 Calculating Measures of Central Tendency
15(1)
1.8.3 Calculating Measures of Variation
15(1)
1.8.4 Visualizing Measures of Variation
16(1)
1.9 Exercises
17(6)
Chapter 2 Descriptive Statistics II: Bivariate and Multivariate Statistics
23(34)
2.1 Introduction
23(1)
2.2 Visualizing Bivariate Statistics
24(5)
2.2.1 Two Categorical Variables
24(1)
2.2.2 Combining Categorical and Continuous Variables
25(1)
2.2.3 Two Continuous Variables
25(2)
2.2.4 Which Variable Should Go on Which Axis?
27(1)
2.2.5 General Comments on Choice of Visualization
28(1)
2.3 Measures of Variation
29(1)
2.3.1 Covariance
29(1)
2.3.2 Covariance Matrix
30(1)
2.4 Correlation
30(7)
2.4.1 Pearson's Correlation Coefficient
30(5)
2.4.2 Spearman's Rank Correlation Coefficient
35(2)
2.4.3 Which Measure of Correlation to Use?
37(1)
2.5 Regression Analysis
37(6)
2.5.1 Using the Best-Fit Line to Make Predictions
39(1)
2.5.2 Fitting Nonlinear Models
40(1)
2.5.3 Fitting Higher-Order Polynomials
40(3)
2.6 Bland-Altman Analysis
43(3)
2.6.1 The Bland-Altman Plot
44(2)
2.7 Summary
46(1)
2.8 Descriptive Bivariate and Multivariate Statistics Using MATLAB
47(5)
2.8.1 Visualizing Bivariate Data
47(1)
2.8.2 Covariance
48(1)
2.8.3 Correlation
49(1)
2.8.4 Calculating Best-Fit Lines
50(1)
2.8.5 Bland-Altman Analysis
51(1)
2.9 Further Resources
52(1)
2.10 Exercises
52(5)
Chapter 3 Descriptive Statistics III: ROC Analysis
57(14)
3.1 Introduction
57(1)
3.2 Notation
58(3)
3.2.1 Sensitivity and Specificity
59(1)
3.2.2 Positive and Negative Predictive Values
59(1)
3.2.3 Example Calculation of Se, Sp, PPV and N PV
60(1)
3.3 ROC Curves
61(2)
3.4 Summary
63(1)
3.5 Using MATLAB for ROC Analysis
63(1)
3.6 Further Resources
64(1)
3.7 Exercises
64(7)
Chapter 4 Inferential Statistics h Basic Concepts
71(20)
4.1 Introduction
71(1)
4.2 Notation
72(1)
4.3 Probability
72(5)
4.3.1 Probabilities of Single Events
73(1)
4.3.2 Probabilities of Multiple Events
74(3)
4.4 Probability Distributions
77(2)
4.4.1 The Normal Distribution
77(2)
4.5 Why the Normal Distribution Is so Important: The Central Limit Theorem
79(1)
4.6 Standard Error of the Mean
80(3)
4.7 Confidence Intervals of the Mean
83(2)
4.8 Summary
85(1)
4.9 Probability Distributions and Measures of Reliability Using MATLAB
85(2)
4.9.1 Probability Distributions
85(1)
4.9.2 Standard Error of the Mean
86(1)
4.9.3 Confidence Interval of the Mean
86(1)
4.10 Further Resources
87(1)
4.11 Exercises
87(4)
Chapter 5 Inferential Statistics II: Parametric Hypothesis Testing
91(28)
5.1 Introduction
91(1)
5.2 Hypothesis Testing
91(2)
5.3 Types of Data for Hypothesis Tests
93(1)
5.4 The r-distribution and Student's t-test
94(1)
5.5 One-Sample Student's t-test
95(4)
5.6 Confidence Intervals for Small Samples
99(4)
5.7 Two Sample Student's t-test
103(4)
5.7.1 Paired Data
103(2)
5.7.2 Unpaired Data
105(2)
5.7.3 Paired vs. Unpaired t-test
107(1)
5.8 1-tailed vs. 2-tailed Tests
107(2)
5.9 Hypothesis Testing with Larger Sample Sizes: The z-test
109(1)
5.10 Summary
110(1)
5.11 Parametric Hypothesis Testing Using MATLAB
111(2)
5.11.1 Student's t-test
111(1)
5.11.2 z-test
112(1)
5.11.3 The t-distribution
112(1)
5.12 Further Resources
113(1)
5.13 Exercises
113(6)
Chapter 6 Inferential Statistics III: Nonparametric Hypothesis Testing
119(28)
6.1 Introduction
119(1)
6.2 Sign Test
120(4)
6.3 Wilcoxon Signed-Rank Test
124(4)
6.4 Mann--Whitney U Test
128(4)
6.5 Chi-Square Test
132(5)
6.5.1 One-Sample Chi-Square Test
132(2)
6.5.2 Two-Sample Chi-Square Test for Independence
134(3)
6.6 Summary
137(1)
6.7 Nonparametric Hypothesis Testing Using MATLAB
138(2)
6.7.1 Sign Test
138(1)
6.7.2 Wilcoxon Signed-Rank Test
138(1)
6.7.3 Mann-Whitney U Test
139(1)
6.7.4 Chi-Square Test
139(1)
6.8 Further Resources
140(1)
6.9 Exercises
141(6)
Chapter 7 Inferential Statistics IV: Choosing a Hypothesis Test
147(26)
7.1 Introduction
147(1)
7.2 Visual Methods to Investigate Whether a Sample Fits a Normal Distribution
148(5)
7.2.1 Histograms
148(1)
7.2.2 Quantile-Quantile Plots
149(4)
7.3 Numerical Methods to Investigate Whether a Sample Fits a Normal Distribution
153(8)
7.3.1 Probability Plot Correlation Coefficient
153(1)
7.3.2 Skew Values
154(1)
7.3.3 z-values
154(2)
7.3.4 Shapiro--Wilk Test
156(2)
7.3.5 Chi-Square Test for Normality
158(3)
7.4 Should We Use a Parametric or Nonparametric Test?
161(1)
7.5 Does It Matter if We Use the Wrong Test?
162(1)
7.6 Summary
163(1)
7.7 Assessing Data Distributions Using MATLAB
164(3)
7.7.1 Visual Methods
164(1)
7.7.2 Numerical Methods
165(2)
7.8 Further Resources
167(1)
7.9 Exercises
167(6)
Chapter 8 Inferential Statistics V: Multiple and Multivariate Hypothesis Testing
173(28)
8.1 Introduction
173(1)
8.2 Multiple Hypothesis Testing
174(9)
8.2.1 Bonferroni's Correction
174(2)
8.2.2 Analysis of Variance (ANOVA)
176(6)
Anova With Unequal Sample Sizes
182(1)
8.3 Multivariate Hypothesis Testing
183(7)
8.3.1 Hotelling's T2 Test
184(4)
Two Sample Hotelling's T1 Test
188(1)
8.3.2 Multivariate Analysis of Variance (MANOVA)
189(1)
8.4 Which Test Should We Use?
190(2)
8.5 Summary
192(1)
8.6 Multiple and Multivariate Hypothesis Testing Using MATLAB
192(4)
8.6.1 Bonferroni's Correction
192(1)
8.6.2 ANOVA
193(1)
8.6.3 Hotelling'sT-Test
193(2)
8.6.4 MANOVA
195(1)
8.7 Further Resources
196(1)
8.8 Exercises
196(5)
Chapter 9 Experimental Design and Sample Size Calculations
201(16)
9.1 Introduction
201(1)
9.2 Experimental and Observational Studies
201(3)
9.2.1 Observational Studies
202(1)
9.2.2 Experimental Studies
202(1)
9.2.3 Showing Cause-and-Effect
203(1)
9.3 Random and Systematic Error [ Bias)
204(1)
9.4 Reducing Random and Systematic Errors
205(3)
9.4.1 Blocking (Matching) Test and Control Subjects
205(1)
9.4.2 Blinding
205(1)
9.4.3 Multiple Measurement
206(1)
9.4.4 Randomization
207(1)
9.5 Sample Size and Power Calculations
208(3)
9.5.1 Illustration of a Power Calculation for a Single Sample t-test
209(1)
9.5.2 Illustration of a Sample Size Calculation
210(1)
9.6 Summary
211(1)
9.7 Power and Sample Size Calculations Using MATLAB
212(1)
9.7.1 Sample Size Calculations
212(1)
9.7.2 Power Calculations
213(1)
9.8 Further Resources
213(1)
9.9 Exercises
213(4)
Chapter 10 Statistical Shape Models
217(12)
10.1 Introduction
217(1)
10.2 SSMs and Dimensionality Reduction
218(2)
10.3 Forming an SSM
220(3)
10.3.1 Parameterize the Shape
220(1)
10.3.2 Align the Centroids
221(1)
10.3.3 Compute the Mean Shape Vector
221(1)
10.3.4 Compute the Covariance Matrix
222(1)
10.3.5 Compute the Eigenvectors and Eigenvalues
222(1)
10.4 Producing New Shapes From an SSM
223(1)
10.5 Biomedical Applications of SSMs
224(1)
10.6 Summary
225(1)
10.7 Statistical Shape Modeling Using MATLAB
226(1)
10.8 Further Resources
226(1)
10.9 Exercises
226(3)
Chapter 11 MATLAB Case Study on Descriptive and Inferential Statistics
229(6)
11.1 Introduction
229(1)
11.2 Data
230(1)
11.3 Part A: Measuring Myocardium Thickness
230(1)
11.4 Part B: Intraobserver Variability
231(1)
11.5 Part C: Sample Analysis
231(1)
11.6 Summary
232(3)
Appendix A: Statistical Tables 235(10)
References 245(2)
Index 247
Dr King has over 20 years of experience of teaching computing courses at university level. He is currently a Reader in the Biomedical Engineering department at King's College London. With Paul Aljabar, he designed and developed the Computer Programming module for Biomedical Engineering students upon which this book was based. The module has been running since 2014 and Andrew still co-organises and teaches on it. Between 2001-2005, Andrew worked as an Assistant Professor in the Computer Science department at Mekelle University in Ethiopia, and was responsible for curriculum development, and design and delivery of a number of computing modules. Andrew's research interests focus mainly on the use of machine learning and artificial intelligence techniques to tackle problems in medical imaging, with a special focus on dynamic imaging data, i.e. moving organs (Google Scholar: https://goo.gl/ZZGrGr, group web site: http://kclmmag.org). Dr. Robert Eckersley is a Senior Lecturer in the School of Biomedical Engineering and Imaging Sciences at Kings College London. His research interests include all aspects of the physics and engineering of medical ultrasound imaging. He has a long standing interest in the development of microbubble contrast agents for quantitative functional imaging with ultrasound. He is currently PI on an EPSRC grant investigating the development of super-resolution strategies for ultrasound imaging and is an co-investigator on the Wellcome and EPSRC funded iFind project http://www.ifindproject.com.