Statistics for biomedical engineers and scientists how to visualize and analyze data
Statistics for Biomedical Engineers and Scientists: How to Analyze and Visualize Data provides an intuitive understanding of the concepts of basic statistics, with a focus on solving biomedical problems. Readers will learn how to understand the fundamental concepts of descriptive and inferential sta...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press, an imprint of Elsevier
[2019]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630574606719 |
Tabla de Contenidos:
- Front Cover
- Statistics for Biomedical Engineers and Scientists
- Copyright
- Dedication
- Contents
- About the Authors
- Preface
- Aims and Motivation
- Learning Objectives
- How to Use This Book
- Instructors
- Students and Researchers
- Web Site Resources
- Contents and Organization
- Acknowledgments
- 1 Descriptive Statistics I: Univariate Statistics
- 1.1 Introduction
- 1.2 Types of Statistical Data
- 1.3 Univariate Data Visualization
- 1.3.1 Dotplot
- 1.3.2 Histogram
- 1.3.3 Bar Chart
- 1.4 Measures of Central Tendency
- 1.4.1 Mean
- 1.4.2 Median
- 1.4.3 Mode
- 1.4.4 Which Measure of Central Tendency to Use?
- 1.5 Measures of Variation
- 1.5.1 Standard Deviation
- 1.5.2 Interquartile Range
- 1.5.3 Which Measure of Variation to Use?
- 1.6 Visualizing Measures of Variation
- 1.6.1 Visualizing Mean and Standard Deviation
- 1.6.2 Visualizing Median and IQR: The Box Plot
- 1.7 Summary
- 1.8 Using MATLAB for Univariate Descriptive Statistics
- 1.8.1 Visualization of Univariate Data
- 1.8.2 Calculating Measures of Central Tendency
- 1.8.3 Calculating Measures of Variation
- 1.8.4 Visualizing Measures of Variation
- 1.9 Exercises
- 2 Descriptive Statistics II: Bivariate and Multivariate Statistics
- 2.1 Introduction
- 2.2 Visualizing Bivariate Statistics
- 2.2.1 Two Categorical Variables
- 2.2.2 Combining Categorical and Continuous Variables
- 2.2.3 Two Continuous Variables
- 2.2.4 Which Variable Should Go on Which Axis?
- 2.2.5 General Comments on Choice of Visualization
- 2.3 Measures of Variation
- 2.3.1 Covariance
- 2.3.2 Covariance Matrix
- 2.4 Correlation
- 2.4.1 Pearson's Correlation Coef cient
- 2.4.2 Spearman's Rank Correlation Coef cient
- 2.4.3 Which Measure of Correlation to Use?
- 2.5 Regression Analysis
- 2.5.1 Using the Best-Fit Line to Make Predictions.
- 2.5.2 Fitting Nonlinear Models
- 2.5.3 Fitting Higher-Order Polynomials
- 2.6 Bland-Altman Analysis
- 2.6.1 The Bland-Altman Plot
- 2.7 Summary
- 2.8 Descriptive Bivariate and Multivariate Statistics Using MATLAB
- 2.8.1 Visualizing Bivariate Data
- 2.8.2 Covariance
- 2.8.3 Correlation
- 2.8.4 Calculating Best-Fit Lines
- 2.8.5 Bland-Altman Analysis
- 2.9 Further Resources
- 2.10 Exercises
- 3 Descriptive Statistics III: ROC Analysis
- 3.1 Introduction
- 3.2 Notation
- 3.2.1 Sensitivity and Speci city
- 3.2.2 Positive and Negative Predictive Values
- 3.2.3 Example Calculation of Se, Sp, PPV and NPV
- 3.3 ROC Curves
- 3.4 Summary
- 3.5 Using MATLAB for ROC Analysis
- 3.6 Further Resources
- 3.7 Exercises
- 4 Inferential Statistics I: Basic Concepts
- 4.1 Introduction
- 4.2 Notation
- 4.3 Probability
- 4.3.1 Probabilities of Single Events
- 4.3.2 Probabilities of Multiple Events
- 4.4 Probability Distributions
- 4.4.1 The Normal Distribution
- 4.5 Why the Normal Distribution Is so Important: The Central Limit Theorem
- 4.6 Standard Error of the Mean
- 4.7 Con dence Intervals of the Mean
- 4.8 Summary
- 4.9 Probability Distributions and Measures of Reliability Using MATLAB
- 4.9.1 Probability Distributions
- 4.9.2 Standard Error of the Mean
- 4.9.3 Con dence Interval of the Mean
- 4.10 Further Resources
- 4.11 Exercises
- 5 Inferential Statistics II: Parametric Hypothesis Testing
- 5.1 Introduction
- 5.2 Hypothesis Testing
- 5.3 Types of Data for Hypothesis Tests
- 5.4 The t-distribution and Student's t-test
- 5.5 One-Sample Student's t-test
- 5.6 Con dence Intervals for Small Samples
- 5.7 Two Sample Student's t-test
- 5.7.1 Paired Data
- 5.7.2 Unpaired Data
- 5.7.3 Paired vs. Unpaired t-test
- 5.8 1-tailed vs. 2-tailed Tests
- 5.9 Hypothesis Testing with Larger Sample Sizes: The z-test.
- 5.10 Summary
- 5.11 Parametric Hypothesis Testing Using MATLAB
- 5.11.1 Student's t-test
- 5.11.2 z-test
- 5.11.3 The t-distribution
- 5.12 Further Resources
- 5.13 Exercises
- 6 Inferential Statistics III: Nonparametric Hypothesis Testing
- 6.1 Introduction
- 6.2 Sign Test
- 6.3 Wilcoxon Signed-Rank Test
- 6.4 Mann-Whitney U Test
- 6.5 Chi-Square Test
- 6.5.1 One-Sample Chi-Square Test
- 6.5.2 Two-Sample Chi-Square Test for Independence
- 6.6 Summary
- 6.7 Nonparametric Hypothesis Testing Using MATLAB
- 6.7.1 Sign Test
- 6.7.2 Wilcoxon Signed-Rank Test
- 6.7.3 Mann-Whitney U Test
- 6.7.4 Chi-Square Test
- 6.8 Further Resources
- 6.9 Exercises
- 7 Inferential Statistics IV: Choosing a Hypothesis Test
- 7.1 Introduction
- 7.2 Visual Methods to Investigate Whether a Sample Fits a Normal Distribution
- 7.2.1 Histograms
- 7.2.2 Quantile-Quantile Plots
- 7.3 Numerical Methods to Investigate Whether a Sample Fits a Normal Distribution
- 7.3.1 Probability Plot Correlation Coef cient
- 7.3.2 Skew Values
- 7.3.3 z-values
- 7.3.4 Shapiro-Wilk Test
- 7.3.5 Chi-Square Test for Normality
- 7.4 Should We Use a Parametric or Nonparametric Test?
- 7.5 Does It Matter if We Use the Wrong Test?
- 7.6 Summary
- 7.7 Assessing Data Distributions Using MATLAB
- 7.7.1 Visual Methods
- 7.7.2 Numerical Methods
- 7.8 Further Resources
- 7.9 Exercises
- 8 Inferential Statistics V: Multiple and Multivariate Hypothesis Testing
- 8.1 Introduction
- 8.2 Multiple Hypothesis Testing
- 8.2.1 Bonferroni's Correction
- 8.2.2 Analysis of Variance (ANOVA)
- ANOVA With Unequal Sample Sizes
- 8.3 Multivariate Hypothesis Testing
- 8.3.1 Hotelling's T2 Test
- Two Sample Hotelling's T2 Test
- 8.3.2 Multivariate Analysis of Variance (MANOVA)
- 8.4 Which Test Should We Use?
- 8.5 Summary.
- 8.6 Multiple and Multivariate Hypothesis Testing Using MATLAB
- 8.6.1 Bonferroni's Correction
- 8.6.2 ANOVA
- 8.6.3 Hotelling's T2 Test
- 8.6.4 MANOVA
- 8.7 Further Resources
- 8.8 Exercises
- 9 Experimental Design and Sample Size Calculations
- 9.1 Introduction
- 9.2 Experimental and Observational Studies
- 9.2.1 Observational Studies
- 9.2.2 Experimental Studies
- 9.2.3 Showing Cause-and-Effect
- 9.3 Random and Systematic Error (Bias)
- 9.4 Reducing Random and Systematic Errors
- 9.4.1 Blocking (Matching) Test and Control Subjects
- 9.4.2 Blinding
- 9.4.3 Multiple Measurement
- 9.4.4 Randomization
- 9.5 Sample Size and Power Calculations
- 9.5.1 Illustration of a Power Calculation for a Single Sample t-test
- 9.5.2 Illustration of a Sample Size Calculation
- 9.6 Summary
- 9.7 Power and Sample Size Calculations Using MATLAB
- 9.7.1 Sample Size Calculations
- 9.7.2 Power Calculations
- 9.8 Further Resources
- 9.9 Exercises
- 10 Statistical Shape Models
- 10.1 Introduction
- 10.2 SSMs and Dimensionality Reduction
- 10.3 Forming an SSM
- 10.3.1 Parameterize the Shape
- 10.3.2 Align the Centroids
- 10.3.3 Compute the Mean Shape Vector
- 10.3.4 Compute the Covariance Matrix
- 10.3.5 Compute the Eigenvectors and Eigenvalues
- 10.4 Producing New Shapes From an SSM
- 10.5 Biomedical Applications of SSMs
- 10.6 Summary
- 10.7 Statistical Shape Modeling Using MATLAB
- 10.8 Further Resources
- 10.9 Exercises
- 11 MATLAB Case Study on Descriptive and Inferential Statistics
- 11.1 Introduction
- 11.2 Data
- 11.3 Part A: Measuring Myocardium Thickness
- 11.4 Part B: Intraobserver Variability
- 11.5 Part C: Sample Analysis
- 11.6 Summary
- A Statistical Tables
- References
- Index
- Back Cover.
