Simple Regression Analysis in Public Health from Coursera

What's inside

Syllabus

Simple Regression Methods

Module one covers simple regression, the four different types of regression, commonalities between them, and simple linear aggression. Before completing the graded quiz, you can test your knowledge with the practice quiz.

Simple Logistic Regression

Within module two, we will look at logistic regression, create confidence intervals, and estimate p-values. You will have the opportunity to test your knowledge in both a practice quiz and a graded quiz.

Simple Cox Proportional Hazards Regression

Module three focuses on Cox regression with different predictors. You will have the opportunity to test your knowledge first with the practice quiz and, then, with the graded quiz.

Confounding, Adjustment, and Effect Modification

Within module four, you will look at confounding and adjustment, and unadjusted and adjusted association estimates. Additionally, you will learn about effect modification. Similar to previous modules, you will first take a practice quiz before completing the graded quiz.

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Examines biostatistics, a key component of life sciences.

Utilizes simple and understandable regression methods like simple linear, logistic, and Cox regression

Introduces statistical analysis and interpretation of data from published studies

Enables learners to interpret confounding, adjustment, and effect modification in health research

Emphasizes interpreting and performing statistical calculations on real data

Course may require prior knowledge in statistical concepts and methods.

Reviews summary

An introduction to simple regression analysis

Learners say Simple Regression Analysis in Public Health is a well-received course for those looking to learn more about regression analysis in public health. The course is largely positive with reviewers praising the engaging assignments and informative content. According to students, Instructor Dr. John McGready does a phenomenal job explaining complex topics in a way that is easy to understand. The course is well-suited for those with little to no background in biostatistics and provides a strong foundation for interpreting data analysis in literature.

Provide a good challenge and help reinforce learning.

"I really enjoyed the course; I wish there were more practice questions and more detailed analysis..."

"Very happy that all I requested for was attended to i.e explanation especially of the summative assessment..."

Organized and clearly structured lessons.

"The course is well-structured for people who study alone... looking for support elsewhere."

"The Question and Answer section needs a few corrections... The order of the lectures is coherent and leads the learner to achieve the objectives."

Course has some difficult concepts, but is manageable.

"Another great course! A bit tougher than the previous 2 but workable."

"I think this level is relatively more difficult than the previous two but manageable..."

"Such complex concepts explained with ease."

Content is informative, engaging, and practical.

"Great as always wih Dr. John...."

"Very informative and well delivered course with clear instructions and mind teasing quizzes."

"Good explanations and challenging to execute tasks"

"Fantastic course! Great work!"

"Very good lectures with multiple examples from literature."

Dr. McGready is an excellent instructor.

"Excellent... He is a real professor."

"Professor McGready is amazing and explains things very well"

"McGready is an excellent lecturer! Really takes the time to explain concepts in detail"

"I never thought I would really understand statistics... Johns series of courses really give those numbers meaning"

Some minor technical issues, but overall the course is well-produced.

"The calculation has not been the problem."

"There are a number of small typos in the slides and quizzes"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Simple Regression Analysis in Public Health with these activities:

Review the basics of statistical reasoning

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This activity will help you refresh the basics of statistical reasoning so that you have a strong foundation for the rest of the course.

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Review the concepts of probability and distributions.
Practice solving simple statistical problems.

Create a summary of the course materials

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Creating a summary of the course materials will help you to retain what you have learned. This activity will also give you a valuable resource to refer back to later.

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Review the course materials.
Identify the key concepts.
Write a summary of the key concepts.

Practice creating scatterplots

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Creating scatterplots is an essential skill for visualizing and understanding the relationship between two variables in biostatistics. This activity will give you an opportunity to practice creating scatterplots and interpreting the results.

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Choose two variables to graph.
Generate a scatterplot in your preferred statistical software.
Draw a line of best fit to visualize the relationship between the variables.
Interpret the results of the scatter plot.

Four other activities

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Learn about the different types of logistic regression

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Logistic regression is a valuable tool for analyzing binary and categorical outcomes in biostatistics. This tutorial will introduce you to the different types of logistic regression and how to use them to model your data.

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Identify the type of logistic regression that is appropriate for your data.
Fit the model to your data.
Interpret the results of the model.

Discussion board - Discuss the use of confounding variables in biostatistics

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Confounding variables are a common challenge in biostatistics. This discussion board will give you an opportunity to learn about confounding variables and how to adjust for them in your analysis.

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Read the assigned readings on confounding variables.
Post a discussion question to the board.
Respond to at least two other students' posts.

Write a short report on your logistic regression analysis results

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Writing a short report will help you to synthesize what you have learned in the logistic regression module. This activity will give you an opportunity to practice communicating your results to a broader audience.

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Summarize the goals of your logistic regression analysis.
Describe the data you used and the methods you employed.
Interpret the results of your analysis.
Draw conclusions about the relationship between the variables in your study.

Identify and adjust for confounding variables in your own research

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This project will give you an opportunity to apply what you have learned about confounding variables to your own research. You will identify potential confounding variables and develop strategies to adjust for them in your analysis.

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Identify the potential confounding variables in your research.
Develop a strategy to adjust for the confounding variables.
Implement the strategy in your analysis.
Interpret the results of your analysis.

Career center

Learners who complete Simple Regression Analysis in Public Health will develop knowledge and skills that may be useful to these careers:

Medical Statistician

Medical Statisticians use statistical methods to design and analyze clinical trials and other medical research studies. This course will provide aspiring Medical Statisticians with a strong foundation in the statistical methods used in medical research, including simple regression methods.

See salaries and explore the career path for Medical Statistician

Statistician

Statisticians use statistical methods to collect, analyze, and interpret data. This course will provide aspiring Statisticians with a strong foundation in the statistical methods used in various fields, including simple regression methods.

See salaries and explore the career path for Statistician

Biostatistician

A Biostatistician will be able to play a major role in the field of public health by analyzing and interpreting large datasets to identify trends, patterns, and relationships. This course can help aspiring Biostatisticians to understand the concepts of simple regression methods and other relevant statistical methods used in public health research.

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Public Health Analyst

Public Health Analysts use statistical methods to analyze and interpret public health data. This course will provide aspiring Public Health Analysts with a strong foundation in the statistical methods used in public health research, including simple regression methods.

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Epidemiologist

Epidemiologists study the causes and patterns of health and disease in populations. This course will provide aspiring Epidemiologists with a strong foundation in the statistical methods used in epidemiological research, including simple regression methods.

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Data Scientist

Data Scientists use their knowledge of statistics and modeling to extract meaningful insights from data. The course's focus on simple regression methods will help aspiring Data Scientists understand the fundamentals of statistical modeling, which is a crucial skill for success in this field.

See salaries and explore the career path for Data Scientist

Research Scientist

Research Scientists use scientific methods to conduct research and develop new knowledge. This course will provide aspiring Research Scientists with a strong foundation in the statistical methods used in scientific research, including simple regression methods.

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Health Economist

Health Economists use economic principles to analyze and evaluate health care systems and policies. This course will help aspiring Health Economists understand the use of statistical methods, such as simple regression, in health economics research.

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Pharmacist

Pharmacists use their knowledge of drugs and their effects to provide patient care. This course will help aspiring Pharmacists understand the use of statistical methods, such as simple regression, in pharmaceutical research and development.

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Health Informatician

Health Informaticians use their knowledge of health care and information technology to design and implement health information systems. This course may be useful for aspiring Health Informaticians who are interested in using statistical methods to analyze health data.

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Biomedical Engineer

Biomedical Engineers apply engineering principles to design and develop medical devices and systems. This course may be useful for aspiring Biomedical Engineers who are interested in using statistical methods in their research and development work.

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Clinical Research Coordinator

Clinical Research Coordinators assist with the design, implementation, and management of clinical trials. This course may be useful for aspiring Clinical Research Coordinators who are interested in understanding the statistical methods used in clinical research.

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Health Services Researcher

Health Services Researchers study the organization, delivery, and financing of health care services. This course may be useful for aspiring Health Services Researchers who are interested in using statistical methods to evaluate the effectiveness and efficiency of health care interventions.

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Medical Writer

Medical Writers create written content about medical topics for a variety of audiences. This course may be useful for aspiring Medical Writers who are interested in understanding the statistical methods used in medical research and communicating the results to a non-technical audience.

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Nurse Practitioner

Nurse Practitioners provide primary care to patients across the lifespan. This course may be useful for aspiring Nurse Practitioners who are interested in using statistical methods to evaluate the effectiveness of nursing interventions.

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