Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you'll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.
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课程信息
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
您将学到的内容有
Practice multiple regression methods to determine relationships between an outcome and multiple predictors
Use the Spline approach for non-linear relationships with continuous predictors
Perform calculations with multiple predictor variables
您将获得的技能
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
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约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
教学大纲 - 您将从这门课程中学到什么
An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression
Within this module, an overview of multiple regression will be provided. Additionally, examples and applications will be examined. A practice quiz is provided to test your knowledge before completing the graded quiz.
Multiple Logistic Regression
Module two covers examples of multiple logistic regression, basics of model estimates, and a discussion of effect modification. In addition to lectures, you will also be completing a practice quiz and graded quiz.
Multiple Cox Regression
The last module for this class focuses on multiple Cox regression, the “Linearity” assumption, examples, and applications. You will complete a practice quiz, graded quiz, and project.
Course Project
During this module, you get the chance to demonstrate what you've learned by putting yourself in the shoes of biostatistical consultant on two different studies, one about self-administration of injectable contraception and one about medical appointment scheduling in Brazil. The two research teams have asked you to help them interpret previously published results in order to inform the planning of their own studies. If you've already taken other courses in this specialization, then this scenario will be familiar.
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来自MULTIPLE REGRESSION ANALYSIS IN PUBLIC HEALTH 的热门评论
lecture pattern gives enthusiasm in learning and solving quiz sessions. explanations for the solutions gives immense strength and confidence in learning. Excellant course and marvellous experience
This course covers all types of Multiple Regressions. Instructor explained the complex topics in simple language. Relevant examples from clinical field and thorough explanation by the Instructor.
The course is well structured. The numerous examples and practice questions help the student apply the concepts learned. I love the fact that almost every question was from a real article.
Excellent Course. The video lectures are thorough and provide many examples to help explain the concepts. If this professor produces more courses, I will certainly take them.
关于 Biostatistics in Public Health 专项课程
This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.

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