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Brian Zaharatos
This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation.
Read more
This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation.
Read more
This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation.
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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What's inside
Syllabus
Introduction to ANOVA and Experimental Design
In this module, we will introduce the basic conceptual framework for experimental design and define the models that will allow us to answer meaningful questions about the differences between group means with respect to a continuous variable. Such models include the one-way Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) models.
Read more
Syllabus
Introduction to ANOVA and Experimental Design
In this module, we will introduce the basic conceptual framework for experimental design and define the models that will allow us to answer meaningful questions about the differences between group means with respect to a continuous variable. Such models include the one-way Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) models.
Read more
Hypothesis Testing in the ANOVA Context
In this module, we will learn how statistical hypothesis testing and confidence intervals, in the ANOVA/ANCOVA context, can help answer meaningful questions about the differences between group means with respect to a continuous variable.
Two-Way ANOVA and Interactions
In this module, we will study the two-way ANOVA model and use it to answer research questions using real data.
Experimental Design: Basic Concepts and Designs
In this module, we will study fundamental experimental design concepts, such as randomization, treatment design, replication, and blocking. We will also look at basic factorial designs as an improvement over elementary “one factor at a time” methods. We will combine these concepts with the ANOVA and ANCOVA models to conduct meaningful experiments.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops an understanding of the Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) models, which are fundamental to data science applications
Provides a thorough examination of the two-way ANOVA model and its use in answering research questions using real data
Teaches fundamental experimental design concepts, including randomization, treatment design, replication, and blocking, which are essential for conducting meaningful experiments
Covers important topics in experimental design, such as the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design
Taught by Brian Zaharatos, an experienced instructor in statistical modeling
Aligned with CU Boulder's Master of Science in Data Science (MS-DS) degree, a renowned program in the field
Save this course
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Reviews summary
Quality anova course
Learners say this course is great and practical with engaging assignments. Its focus on experimental design is useful for researchers and data scientists
Learners thought the exercises in this course were useful.
"Really useful and practical, and the exercise is not too difficult."
Learners say this course is good for those in research.
"Great course for the research field"
"I could gain insight enough for my work in data science"
Learners describe this course as great and practical.
"Great course. Really useful and practical"
"High quality. Intensive course."
The course covers experimental design, but could be improved.
"The experimental design could be imporved but they give a general presentation."
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 ANOVA and Experimental Design with these
activities:
Read 'Experimental Design for the Life Sciences' by Olive
Show steps
Provides a comprehensive overview of experimental design principles and applications, enhancing students' ability to design and conduct effective experiments.
View
Applied Cognitive Psychology
on Amazon
Show steps
-
Read Chapter 2: Basic Principles of Experimental Design.
-
Review the examples and case studies to understand the practical implications of experimental design.
Build a research library on experimental design and ANOVA
Show steps
Encourages students to gather and organize resources, fostering a deeper understanding of key concepts and practical applications.
Browse courses on
Experimental Design
Show steps
-
Identify reputable sources (e.g., journals, textbooks, online articles).
-
Organize the resources by topic and relevance.
-
Annotate the resources with key takeaways and insights.
Review variances
Show steps
Reinforces understanding of variance and its role in statistical analysis.
Browse courses on
Variance
Show steps
-
Calculate variances for different datasets using statistical software or online calculators.
-
Analyze the results and identify patterns and trends.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss ANOVA assumptions and potential violations
Show steps
Promotes peer-learning and helps students identify and mitigate potential issues in ANOVA analysis.
Browse courses on
ANOVA
Show steps
-
Form a study group with classmates.
-
Review the assumptions of ANOVA.
-
Discuss real-world examples of potential violations.
-
Brainstorm strategies to address violations.
Complete DataCamp course on ANOVA
Show steps
Offers interactive exercises and real-world examples to deepen understanding of ANOVA concepts and applications.
Browse courses on
ANOVA
Show steps
-
Enroll in the DataCamp course 'ANOVA in R'.
-
Complete the modules and quizzes.
-
Apply the learned techniques to analyze datasets.
Attend a workshop on experimental design and statistical analysis
Show steps
Provides students with the opportunity to engage with experts and learn from their practical experiences.
Browse courses on
Experimental Design
Show steps
-
Identify a relevant workshop offered by universities or professional organizations.
-
Register and attend the workshop.
-
Actively participate in discussions and ask questions.
Develop an experimental design for a research question
Show steps
Enhances students' ability to translate research questions into testable hypotheses and design experiments that can provide meaningful data.
Browse courses on
Experimental Design
Show steps
-
Identify a research question of interest.
-
Formulate a hypothesis to test.
-
Determine the appropriate experimental design (e.g., ANOVA, ANCOVA).
-
Specify the variables, treatments, and sample size.
Apply ANOVA to analyze real-world data
Show steps
Provides practical experience in applying ANOVA to real-world datasets, enhancing students' ability to interpret and communicate statistical results.
Browse courses on
ANOVA
Show steps
-
Obtain a real-world dataset with multiple groups and a continuous outcome variable.
-
Conduct an ANOVA analysis using statistical software (e.g., R, Python).
-
Interpret the ANOVA results and draw meaningful conclusions.
-
Communicate the findings in a written report or presentation.
Read 'Experimental Design for the Life Sciences' by Olive
Show steps
Provides a comprehensive overview of experimental design principles and applications, enhancing students' ability to design and conduct effective experiments.
View
Applied Cognitive Psychology
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 2: Basic Principles of Experimental Design.
- Review the examples and case studies to understand the practical implications of experimental design.
Build a research library on experimental design and ANOVA
Show steps
Encourages students to gather and organize resources, fostering a deeper understanding of key concepts and practical applications.
Browse courses on
Experimental Design
Show steps
Show steps
Show steps
- Identify reputable sources (e.g., journals, textbooks, online articles).
- Organize the resources by topic and relevance.
- Annotate the resources with key takeaways and insights.
Review variances
Show steps
Reinforces understanding of variance and its role in statistical analysis.
Browse courses on
Variance
Show steps
Show steps
Show steps
- Calculate variances for different datasets using statistical software or online calculators.
- Analyze the results and identify patterns and trends.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss ANOVA assumptions and potential violations
Show steps
Promotes peer-learning and helps students identify and mitigate potential issues in ANOVA analysis.
Browse courses on
ANOVA
Show steps
Show steps
Show steps
- Form a study group with classmates.
- Review the assumptions of ANOVA.
- Discuss real-world examples of potential violations.
- Brainstorm strategies to address violations.
Complete DataCamp course on ANOVA
Show steps
Offers interactive exercises and real-world examples to deepen understanding of ANOVA concepts and applications.
Browse courses on
ANOVA
Show steps
Show steps
Show steps
- Enroll in the DataCamp course 'ANOVA in R'.
- Complete the modules and quizzes.
- Apply the learned techniques to analyze datasets.
Attend a workshop on experimental design and statistical analysis
Show steps
Provides students with the opportunity to engage with experts and learn from their practical experiences.
Browse courses on
Experimental Design
Show steps
Show steps
Show steps
- Identify a relevant workshop offered by universities or professional organizations.
- Register and attend the workshop.
- Actively participate in discussions and ask questions.
Develop an experimental design for a research question
Show steps
Enhances students' ability to translate research questions into testable hypotheses and design experiments that can provide meaningful data.
Browse courses on
Experimental Design
Show steps
Show steps
Show steps
- Identify a research question of interest.
- Formulate a hypothesis to test.
- Determine the appropriate experimental design (e.g., ANOVA, ANCOVA).
- Specify the variables, treatments, and sample size.
Apply ANOVA to analyze real-world data
Show steps
Provides practical experience in applying ANOVA to real-world datasets, enhancing students' ability to interpret and communicate statistical results.
Browse courses on
ANOVA
Show steps
Show steps
Show steps
- Obtain a real-world dataset with multiple groups and a continuous outcome variable.
- Conduct an ANOVA analysis using statistical software (e.g., R, Python).
- Interpret the ANOVA results and draw meaningful conclusions.
- Communicate the findings in a written report or presentation.
Career center
Learners who complete ANOVA and Experimental Design will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
A Statistician collects, interprets, and presents data that helps businesses and organizations make informed decisions. Professionals in this field heavily rely on statistical methods, including ANOVA, to understand data and provide insights. This course will strengthen your understanding of the foundational principles of ANOVA, providing you with a practical understanding of how to apply it in the field of statistics.
Data Scientist
Data Scientist
A Data Scientist applies principles of statistics, mathematics, and computing to gather and analyze large datasets. In the field of data science, professionals often use ANOVA and other techniques to uncover patterns and relationships within data. This course will help you build a foundation for understanding the underlying principles of ANOVA, which will provide you with a valuable analytical tool to solve complex data-driven problems in the field of data science.
Biostatistician
Biostatistician
A Biostatistician collects and analyzes data in the field of biology, utilizing statistical principles to address complex scientific questions. Professionals in this field often rely on techniques such as ANOVA to draw meaningful conclusions from experimental data. This course will enhance your understanding of ANOVA, providing you with a valuable tool to conduct robust statistical analyses in the field of biostatistics.
Research Scientist
Research Scientist
A Research Scientist conducts experiments and gathers data to advance knowledge within a specific scientific field. Professionals in this role utilize analytical methods like ANOVA to analyze research findings and draw well-supported conclusions. This course will provide you with a solid conceptual framework for experimental design using ANOVA, enabling you to design and conduct meaningful experiments in your research endeavors.
Data Analyst
Data Analyst
A Data Analyst collects, processes, and analyzes data to generate insights that help organizations make better decisions. This course will introduce you to the fundamental concepts of ANOVA, providing you with a solid understanding of how to apply this technique to effectively analyze data and extract meaningful insights.
Market Research Analyst
Market Research Analyst
A Market Research Analyst studies market trends and customer behavior to provide insights that assist businesses in making strategic decisions. Understanding research methods like ANOVA is vital in this field, allowing professionals to analyze market data effectively and draw informed conclusions. This course will strengthen your grasp of ANOVA, enhancing your ability to conduct robust market research and develop impactful data-driven strategies.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of health-related events and diseases within populations. Professionals in this field often rely on statistical techniques like ANOVA to analyze epidemiological data and identify factors influencing disease occurrence. This course will provide you with a solid foundation in ANOVA, empowering you to conduct robust epidemiological studies and draw evidence-based conclusions.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst applies mathematical and statistical models to analyze financial data and make investment decisions. This course will introduce you to the fundamental principles of ANOVA, providing you with a valuable analytical tool to assess the impact of different factors on financial performance and make data-driven investment decisions.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to improve the efficiency and effectiveness of business processes. ANOVA is a key method in this field, allowing professionals to evaluate the impact of different factors on operational outcomes. This course will provide you with a strong foundation in ANOVA, enabling you to contribute effectively to optimizing business operations.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and manages risks faced by businesses or organizations. Statistical techniques like ANOVA are essential in this field, helping professionals analyze data and quantify the likelihood and impact of potential risks. This course will enhance your understanding of ANOVA, providing you with a valuable tool to conduct robust risk assessments and make informed decisions.
Financial Analyst
Financial Analyst
A Financial Analyst researches and analyzes financial data to provide insights and recommendations for investment or financial planning. This course will introduce you to the fundamentals of ANOVA, equipping you to analyze financial data effectively and make informed investment decisions.
Business Analyst
Business Analyst
A Business Analyst uses data and analytical techniques to identify and solve business problems. This course will introduce you to the fundamental concepts of ANOVA, providing you with a valuable tool to analyze business data effectively and develop evidence-based solutions.
Survey Researcher
Survey Researcher
A Survey Researcher designs, conducts, and analyzes surveys to collect data on various topics. This course will provide you with a solid foundation in ANOVA, empowering you to design effective surveys, analyze survey data, and draw meaningful conclusions.
Biomedical Engineer
Biomedical Engineer
A Biomedical Engineer applies engineering principles to solve problems in biology and medicine. This course may be helpful in understanding experimental design and data analysis in the context of biomedical research.
Technical Writer
Technical Writer
A Technical Writer creates instruction manuals, technical reports, and other documentation to explain complex technical information. This course may be helpful if you wish to specialize in writing about scientific research or statistical analysis.
For more career information including salaries, visit:
OpenCourser.com/course/r5t3nl/anova
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
ANOVA and Experimental Design.
This textbook provides a comprehensive treatment of statistical inference, including ANOVA, ANCOVA, and experimental design. It valuable reference for students who seek a deep understanding of the theory and applications of statistical inference.
This textbook provides a comprehensive treatment of experimental design and analysis, including ANOVA, ANCOVA, and factorial designs. It valuable reference for students who seek a deeper understanding of the principles and techniques used in designing and analyzing experiments.
This textbook provides a solid theoretical grounding in the theory of linear models. It is useful as a supplement for students who seek a deeper knowledge of the underlying mathematical and statistical concepts that underpin ANOVA and ANCOVA. The textbook also provides practical guidance on the use of the R statistical software for implementing linear models.
This textbook provides a comprehensive treatment of the statistical analysis of designed experiments, including ANOVA, ANCOVA, and factorial designs. It valuable reference for students who seek a deeper understanding of the statistical methods used in experimental design.
This textbook covers a broad range of statistical methods commonly used in psychology, including ANOVA, ANCOVA, and experimental design. It valuable reference for students who seek a comprehensive understanding of these topics and their applications in psychological research.
This textbook provides a comprehensive introduction to biostatistics, including ANOVA, ANCOVA, and experimental design. It valuable resource for students who seek a strong foundation in the statistical methods used in biomedical research.
This textbook provides a comprehensive introduction to statistics, including ANOVA, ANCOVA, and experimental design, with a focus on applications in agricultural sciences. It valuable resource for students who seek to apply statistical methods to agricultural research.
This textbook is specifically tailored to the needs of psychology students and researchers. It provides a clear and concise introduction to experimental design, ANOVA, and ANCOVA, with a focus on practical applications.
This textbook provides a comprehensive introduction to statistics, including ANOVA, ANCOVA, and experimental design, with a focus on applications in engineering and science. It valuable resource for students who seek to apply statistical methods to engineering and scientific research.
This textbook provides a comprehensive introduction to statistics, including ANOVA, ANCOVA, and experimental design, with a focus on applications in social and behavioral sciences. It valuable resource for students who seek to apply statistical methods to research in these fields.
This textbook provides a gentle introduction to statistics, including ANOVA, ANCOVA, and experimental design, using the R statistical software. It valuable resource for students who are new to statistics and seek to learn the basics using a popular statistical software.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/r5t3nl/anova
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Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Brian Zaharatos
Language
English
Transcripts
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This course belongs to a
collection
called
Statistical Modeling for Data Science Applications. It's comprised of three courses:
- Generalized Linear Models and Nonparametric Regression
- ANOVA and Experimental Design (viewing)
- Modern Regression Analysis in R
Good to know
Develops an understanding of the Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) models, which are fundamental to data science applications
Provides a thorough examination of the two-way ANOVA model and its use in answering research questions using real data
Teaches fundamental experimental design concepts, including randomization, treatment design, replication, and blocking, which are essential for conducting meaningful experiments
Covers important topics in experimental design, such as the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design
Taught by Brian Zaharatos, an experienced instructor in statistical modeling
Aligned with CU Boulder's Master of Science in Data Science (MS-DS) degree, a renowned program in the field
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