Multivariate Statistics Made Simple A Practical Approach 1st Edition by Sarma, Vishnu Vardhan – Ebook PDF Instant Download/Delivery: 113861095X , 978-1138610958
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Product details:
ISBN 10: 113861095X
ISBN 13: 978-1138610958
Author: Sarma, Vishnu Vardhan
This book explains the advanced but essential concepts of Multivariate Statistics in a practical way while touching the mathematical logic in a befitting manner. The illustrations are based on real case studies from a super specialty hospital where active research is going on.
Multivariate Statistics Made Simple A Practical Approach 1st Table of contents:
1 Multivariate Statistical Analysis—An Overview
1.1 An Appraisal of Statistical Analysis
1.2 Structure of Multivariate Problems
1.3 Univariate Data Description
1.4 Standard Error (SE) and Confidence Interval (CI)
1.5 Multivariate Descriptive Statistics
1.6 Covariance Matrix and Correlation Matrix
1.7 Data Visualization
1.8 The Multivariate Normal Distribution
1.9 Some Interesting Applications of Multivariate Analysis
Summary
Do it yourself
Suggested Reading
2 Comparison of Multivariate Means
2.1 Multivariate Comparison of Mean Vectors
2.2 One-sample Hotelling’s T2 Test
2.3 Confidence Intervals for Component Means
2.4 Two-Sample Hotelling’s T2 Test
2.5 Paired Comparison of Multivariate Mean Vectors
Summary
Do it yourself
Suggested Reading
3 Analysis of Variance with Multiple Factors
3.1 Review of Univariate Analysis of Variance (ANOVA)
3.2 Multifactor ANOVA
3.3 ANOVA with a General Linear Model
3.4 Continuous Covariates and Adjustment
3.5 Non-Parametric Approach to ANOVA
3.6 Influence of Random Effects on ANOVA
Summary
Do it yourself
Suggested Reading
4 Multivariate Analysis of Variance (MANOVA)
4.1 Simultaneous ANOVA of Several Outcome Variables
4.2 Test Procedure for MANOVA
4.3 Interpreting the Output
4.4 MANOVA with Age as a Covariate
4.5 Using Age and BMI as Covariates
4.6 Theoretical Model for Prediction
Summary
Do it yourself
Suggested Reading
5 Analysis of Repeated Measures Data
5.1 Experiments with Repeated Measures
5.2 RM ANOVA Using SPSS
5.3 RM ANOVA Using MedCalc
5.4 RM ANOVA with One Grouping Factor
5.5 Profile Analysis
One sample profile analysis
Summary
Do it yourself
Suggested Reading
6 Multiple Linear Regression Analysis
6.1 The Concept of Regression
6.2 Multiple Linear Regression
6.3 Selection of Appropriate Variables into the Model
6.4 Predicted Values from the Model
6.5 Quality of Residuals
6.6 Regression Model with Selected Records
Summary
Do it yourself
Suggested Reading
7 Classification Problems in Medical Diagnosis
7.1 The Nature of Classification Problems
7.2 Binary Classifiers and Evaluation of Outcomes
7.3 Performance Measures of Classifiers
7.4 ROC Curve Analysis
7.5 Composite Classifiers
7.6 Biomarker Panels and Longitudinal Markers
Summary
Do it yourself
Suggested Reading
8 Binary Classification with Linear Discriminant Analysis
8.1 The Problem of Discrimination
8.2 The Discriminant Score and Decision Rule
8.3 Understanding the Output
8.4 ROC Curve Analysis of Discriminant Score
8.5 Extension of Binary Classification
Summary
Do it yourself
Suggested Reading
9 Logistic Regression for Binary Classification
9.1 Introduction
9.2 Simple Binary Logistic Regression
9.3 Binary Logistic Regression with Multiple Predictors
9.4 Assessment of the Model and Relative Effectiveness of Markers
9.5 Logistic Regression with Interaction Terms
Summary
Do it yourself
Suggested Reading
10 Survival Analysis and Cox Regression
10.1 Introduction
10.2 Data Requirements for Survival Analysis
10.3 Estimation of Survival Time with Complete Data (No Censoring)
10.4 The Kaplan-Meier Method for Censored Data
10.5 Cox Regression Model
Summary
Do it yourself
Suggested Reading
11 Poisson Regression Analysis
11.1 Introduction
11.2 General Form of Poisson Regression
11.3 Selection of Variables and Subset Regression
11.4 Poisson Regression Using SPSS
11.5 Applications of the Poisson Regression model
Summary
Do it yourself
Suggested Reading
12 Cluster Analysis and Its Applications
12.1 Data Complexity and the Need for Clustering
12.2 Measures of Similarity and General Approach to Clustering
12.3 Hierarchical Clustering and Dendrograms
12.4 The Impact of Clustering Methods on the Results
12.5 K-Means Clustering
Summary
Do it yourself
Suggested Reading
Index
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Tags: Sarma, Vishnu Vardhan, Multivariate Statistics, Practical Approach