Biostatistical Methods and Applications: Experiment Design, Analysis, and Presenting Findings provides students and researchers with the skills needed to apply biostatistical concepts and implement a wide range of analytical tools. The text is an accessible and intentionally curated resource that instructs readers in designing experiments, considering the results, and presenting statistical findings and views. In twenty-four chapters and additional appendices, the book starts with an introduction to the field of statistics and from there covers experiment design, data coding, and data collection methods. The book also delves into descriptive statistics, probability, statistical distributions, correlations, and regression. Common research techniques such as normality tests, sample size determination, missing data analysis, statistical inference, test of significance, and ANOVA are explained in a clear manner so that readers can successfully understand and apply these methods. Biostatistical Methods and Applications: Experiment Design, Analysis, and Presenting Findings thoroughly explores fundamental topics in biostatistics to guide readers in conducting statistical analysis and interpretation by using the step-by-step procedures and diagrams in the book. This book covers current topics which are being used frequently in teaching and research areas, as well as specific statistical techniques including multivariate analysis, binary logistic regression, and non-parametric methods. This valuable first edition provides the necessary tools and conceptual foundations in quantitative reasoning to efficiently extract information from a vast sea of data, and to communicate research findings in order to support hypotheses and give credibility to research methodology and conclusions. With detailed explanations, illustrative examples and diagrams, and beneficial in-text exercises and solutions to support the study of biostatistics, this textbook will prove useful to students, researchers, and scientists who aim to increase their precision around methods and techniques in this field.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Questionnaire formats and data coding used for epidemiological survey - p. 5 3. Chapter 3. Frequency distribution and data presentation - p. 11 4. Chapter 4. Descriptive statistics - p. 37 5. 4.1 Descriptive statistics - p. 37 6. 4.2 Measurement of central tendency - p. 37 7. 4.3 Best measure of central tendency - p. 55 8. 4.4 Microsoft Excel functions to calculate the measurement of central tendency - p. 56 9. 4.5 Measurement of dispersion - p. 56 10. 4.6 Measures of relative standing (partition values) - p. 65 11. 4.7 Measures of the shape of a distribution - p. 70 12. 4.8 Moments - p. 73 13. 4.9 Microsoft Excel functions to calculate the measurements of dispersion - p. 74 14. 4.10 Calculation of descriptive statistics by SPSS - p. 75 15. Chapter 5. Probability - p. 79 16. Chapter 6. Normality test and data transformation - p. 107 17. Chapter 7. Introduction to sampling - p. 111 18. Chapter 8. Missing data mechanism - p. 133 19. Chapter 9. Statistical inference - p. 139 20. Chapter 10. Test of significance - p. 161 21. 10.1 Hypothesis testing - p. 161 22. 10.2 Test statistic - p. 162 23. 10.3 Test of significance for large samples (Z-test) - p. 162 24. 10.4 Applications of Z-test - p. 162 25. 10.5 Test of significance for small samples (Student’s t-test) - p. 168 26. 10.6 Applications of student’s t-test - p. 168 27. 10.7 Chi-square (χ2) test - p. 182 28. 10.8 F-test - p. 191 29. Chapter 11. Correlation and regression - p. 199 30. Chapter 12. Analysis of variance - p. 237 31. Chapter 13. Design of experiment - p. 255 32. Chapter 14. Multivariate analysis - p. 317 33. Chapter 15. Binary logistic regression - p. 329 34. Chapter 16. Nonparametric tests - p. 331 35. Chapter 17. Observational studies - p. 339 36. Chapter 18. Incidence, prevalence, and risk - p. 345 37. Chapter 19. Odds and odds ratio - p. 349 38. Chapter 20. Cox regression - p. 353 39. Chapter 21. Receiver operating characteristic curve - p. 355 40. Chapter 22. Survival analysis (Kaplan-Meier curves) - p. 359 41. Chapter 23. Heat maps and circos plots - p. 363 42. Chapter 24. Path coefficient - p. 367 43. Appendix: Participant consent form - p. 373 44. Bibliography - p. 375 45. Index - p. 379
Biostatistical Methods and Applications: Experiment Design, Analysis, and Presenting Findings provides students and researchers with the skills needed to apply biostatistical concepts and implement a wide range of analytical tools. The text is an accessible and intentionally curated resource that instructs readers in designing experiments, considering the results, and presenting statistical findings and views. In twenty-four chapters and additional appendices, the book starts with an introduction to the field of statistics and from there covers experiment design, data coding, and data collection methods. The book also delves into descriptive statistics, probability, statistical distributions, correlations, and regression. Common research techniques such as normality tests, sample size determination, missing data analysis, statistical inference, test of significance, and ANOVA are explained in a clear manner so that readers can successfully understand and apply these methods. Biostatistical Methods and Applications: Experiment Design, Analysis, and Presenting Findings thoroughly explores fundamental topics in biostatistics to guide readers in conducting statistical analysis and interpretation by using the step-by-step procedures and diagrams in the book. This book covers current topics which are being used frequently in teaching and research areas, as well as specific statistical techniques including multivariate analysis, binary logistic regression, and non-parametric methods. This valuable first edition provides the necessary tools and conceptual foundations in quantitative reasoning to efficiently extract information from a vast sea of data, and to communicate research findings in order to support hypotheses and give credibility to research methodology and conclusions. With detailed explanations, illustrative examples and diagrams, and beneficial in-text exercises and solutions to support the study of biostatistics, this textbook will prove useful to students, researchers, and scientists who aim to increase their precision around methods and techniques in this field.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Questionnaire formats and data coding used for epidemiological survey - p. 5 3. Chapter 3. Frequency distribution and data presentation - p. 11 4. Chapter 4. Descriptive statistics - p. 37 5. 4.1 Descriptive statistics - p. 37 6. 4.2 Measurement of central tendency - p. 37 7. 4.3 Best measure of central tendency - p. 55 8. 4.4 Microsoft Excel functions to calculate the measurement of central tendency - p. 56 9. 4.5 Measurement of dispersion - p. 56 10. 4.6 Measures of relative standing (partition values) - p. 65 11. 4.7 Measures of the shape of a distribution - p. 70 12. 4.8 Moments - p. 73 13. 4.9 Microsoft Excel functions to calculate the measurements of dispersion - p. 74 14. 4.10 Calculation of descriptive statistics by SPSS - p. 75 15. Chapter 5. Probability - p. 79 16. Chapter 6. Normality test and data transformation - p. 107 17. Chapter 7. Introduction to sampling - p. 111 18. Chapter 8. Missing data mechanism - p. 133 19. Chapter 9. Statistical inference - p. 139 20. Chapter 10. Test of significance - p. 161 21. 10.1 Hypothesis testing - p. 161 22. 10.2 Test statistic - p. 162 23. 10.3 Test of significance for large samples (Z-test) - p. 162 24. 10.4 Applications of Z-test - p. 162 25. 10.5 Test of significance for small samples (Student’s t-test) - p. 168 26. 10.6 Applications of student’s t-test - p. 168 27. 10.7 Chi-square (χ2) test - p. 182 28. 10.8 F-test - p. 191 29. Chapter 11. Correlation and regression - p. 199 30. Chapter 12. Analysis of variance - p. 237 31. Chapter 13. Design of experiment - p. 255 32. Chapter 14. Multivariate analysis - p. 317 33. Chapter 15. Binary logistic regression - p. 329 34. Chapter 16. Nonparametric tests - p. 331 35. Chapter 17. Observational studies - p. 339 36. Chapter 18. Incidence, prevalence, and risk - p. 345 37. Chapter 19. Odds and odds ratio - p. 349 38. Chapter 20. Cox regression - p. 353 39. Chapter 21. Receiver operating characteristic curve - p. 355 40. Chapter 22. Survival analysis (Kaplan-Meier curves) - p. 359 41. Chapter 23. Heat maps and circos plots - p. 363 42. Chapter 24. Path coefficient - p. 367 43. Appendix: Participant consent form - p. 373 44. Bibliography - p. 375 45. Index - p. 379
