FUNDAMENTALS OF BIOSTASTISTICS

Intermediate Level

Enhance your skills with our intermediate biostatistics course, designed to deepen your understanding and prepare you for advanced applications.

Courses

 

  1. Point and Confidence Interval Estimation
  2. Effect of Sample Size on Confidence Interval 
  3. Principles of Hypothesis Testing
  4. Practical Applications of Hypothesis Testing
  5. Power and Sample Size
  6. Nonparametric Tests
  7. Simple Linear Regression Analysis
  8. Multiple Linear Regression Analysis
  9. Multilevel Data Structure
  10. Synopsis of Correlation Analysis
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Point and Confidence Interval Estimation
A confidence interval refers to the probability that a population parameter will fall between a set of values for a certain proportion of times.
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Effect of sample size on confidence interval
The effect of sample size on a confidence interval is crucial in statistical analysis because it directly impacts the precision and reliability.
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Principles of Hypothesis Testing
Hypothesis testing involves making an assumption, collecting relevant data, and using statistical methods to determine if the assumption holds true
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Practical Applications of Hypothesis Testing
One-way analysis of Variance (ANOVA) techniques enable more than two treatments or groups to be compared simultaneously.

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Power and Sample Size
Power and sample size are key in hypothesis testing, affecting a study's ability to detect an effect or difference if present

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Nonparametric Tests
Nonparametric tests are used for data that don’t meet parametric test assumptions and are ideal for ordinal data, small samples, or non-normal distributions.
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Simple Linear Regression Analysis
Regression models explain the behavior of one variable based on changes in other variables.
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Multiple Linear Regression Analysis
Multiple linear regression examines how multiple variables influence one outcome.

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Multilevel Data Structure
Logistic regression is a statistical method used for modeling the relationship between one or more independent variables.
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Synopsis of Correlation Analysis
Correlation analysis is a statistical technique used to measure and evaluate the strength and direction of the relationship between two or more variables.

Description

Biostatistics at the intermediate level delves deeper into the analysis and interpretation of biological and health-related data, focusing on advanced statistical methods to tackle complex research questions in medicine, public health, and biology. This stage emphasizes the application of techniques such as regression analysis, survival analysis, and experimental design in real-world contexts, including clinical trials, epidemiology, and genetics. The goal is to enhance the ability to extract meaningful insights from data, inform health policies, and contribute to improved health outcomes. This multidisciplinary field builds on foundational knowledge in statistics and life sciences, preparing individuals to address pressing health challenges through rigorous data analysis.

Course Structure

      • Course Duration: A period of 27 immersive weeks designed to guide you through live online classes.
      • Learning Hours: 54 hours of learner-instructor interaction
      • Learning Experience: Engage with instructors and other learners in real-time and go through learning path together.
      • Interactive Assessments: Stay on track with fun quizzes, peer collaboration, and hands-on projects.
      • Certification: Showcase your achievement with an official Certificate of Completion at the end of the course!
      • Contact Support to register for two or more courses here

Course Highlight

Target Learners

This training program is designed for individuals or groups of people who work in clinical settings, academia, pharmaceutical industries, Government, corporations, nonprofit organizations, and organizations that work on biological data.

Why this course

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