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Biostatistics with Python

You're reading from   Biostatistics with Python Apply Python for biostatistics with hands-on biomedical and biotechnology projects

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781837630967
Length 374 pages
Edition 1st Edition
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Author (1):
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Darko Medin Darko Medin
Author Profile Icon Darko Medin
Darko Medin
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Table of Contents (24) Chapters Close

Preface 1. Part 1:Introduction to Biostatistics and Getting Started with Python
2. Chapter 1: Introduction to Biostatistics FREE CHAPTER 3. Chapter 2: Getting Started with Python for Biostatistics 4. Chapter 3: Exercise 1 – Cleaning and Describing Data Using Python 5. Chapter 4: Part 1 Exemplar Project – Load, Clean, and Describe Diabetes Data in Python 6. Part 2:Introduction to Python for Biostatistics – Methodology and Examples
7. Chapter 5: Introduction to Python for Biostatistics 8. Chapter 6: Biostatistical Inference Using Hypothesis Tests and Effect Sizes 9. Chapter 7: Predictive Biostatistics Using Python 10. Chapter 8: Part 2 Exercise – T-Test, ANOVA, and Linear and Logistic Regression 11. Chapter 9: Biostatistical Inference and Predictive Analytics Using Cardiovascular Study Data 12. Part 3:Clinical Study Design, Analysis, and Synthesizing Evidence
13. Chapter 10: Clinical Study Design 14. Chapter 11: Survival Analysis in Biomedical Research 15. Chapter 12: Meta-Analysis – Synthesizing Evidence from Multiple Studies 16. Chapter 13: Survival Predictive Analysis and Meta-Analysis Practice 17. Chapter 14: Part 3 Exemplar Project – Meta-Analysis of Survival Data in Clinical Research 18. Part 4:Biological and Statistical Variables and Frameworks, and a Final Practical Project from the Field of Biology
19. Chapter 15: Understanding Biological Variables 20. Chapter 16: Data Analysis Frameworks and Performance for Life Sciences Research 21. Chapter 17: Part 4 Exercise – Performing Statistics for Biology Studies in Python 22. Index 23. Other Books You May Enjoy

Answers for Chapter 1

(A stands for answer)

  • A6. Null hypothesis (H0): There are no risk factors for cardiovascular disease among the studied parameters.

    Alternate hypothesis (Ha): Risk factors are present among the studied parameters.

  • A7. This question would have no concrete hypothesis. Instead, the overall goal of the study is to identify the genes that are highly expressed in lung cancer tissues..
  • A8. We can re-formulate this question into three potential options based on different levels of similarity:
    • The mouse-human genome similarity is low (0-50%).
    • The mouse-human genome similarity is medium (50-90%).
    • The mouse-human genome similarity is high (>90%).
  • A9. To answer this research question, we can formulate it as follows:

    Are Ca, Mg, and K concentrations higher in locality A compared to locality B?

  • A10. Null hypothesis (H0): Water temperature does not affect plankton.

    Alternate hypothesis (Ha): Water temperature affects plankton organisms.

Keep practicing yourself!

You have been reading a chapter from
Biostatistics with Python
Published in: Nov 2024
Publisher: Packt
ISBN-13: 9781837630967
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