This course is part of the Statistical Modeling for Data Science Applications Specialization

Brian Zaharatos

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Part of the Master of Science in Data Science degree
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Statistical Modeling for Data Science Applications SpecializationUniversity of Colorado Boulder

About this Course

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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.

Logo adapted from photo by Vincent Ledvina on Unsplash

Flexible deadlines

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Shareable Certificate

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Course 2 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 39 hours to complete

English

Skills you will gain

Calculus
and probability theory.
Linear Algebra

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 39 hours to complete

English

Instructor

Brian Zaharatos

Director, Professional Master’s Degree in Applied Mathematics

Department of Applied Mathematics

6,660 Learners

3 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

11 hours to complete

Introduction to ANOVA and Experimental Design

11 hours to complete

9 videos (Total 87 min), 2 readings, 12 quizzes

9 videos

Introduction to Experimental Design10mThe One-Way ANOVA and ANCOVA Models6mANOVA Variance Decomposition8mANOVA Sums of Squares and the F-test14mANOVA and ANCOVA as Regression Models10mOne-Way ANOVA Interpretation in the Regression Context10mThe ANCOVA Model15mANCOVA with Interactions7mANCOVA with Interactions in R4m

2 readings

Welcome and Where to Find Help 10mInformation on Peer Reviews10m

9 practice exercises

Introduction to ANOVA and Experimental Design30mThe One-Way ANOVA and ANCOVA Models30mANOVA Variance Decomposition30mANOVA Sums of Squares and the F-Test30mANOVA and ANCOVA as Regression Models30mOne-Way ANOVA Interpretation in the Regression Context30mThe ANCOVA Model30mANCOVA with Interactions30mANCOVA with Interactions in R30m

Week2

Week 2

9 hours to complete

Hypothesis Testing in the ANOVA Context

9 hours to complete

6 videos (Total 91 min), 2 readings, 6 quizzes

6 videos

Beyond the Full F-test12mPlanned Comparisons: Defining Contrasts16mPlanned Comparisons: Hypothesis Testing with Contrasts14mPost Hoc Comparisons13mPost Hoc Comparisons in R16mType II Error and Power in the ANOVA Context18m

2 readings

Patrizio E. Tressoldi and David Giofré: "The pervasive avoidance of prospective statistical power: major consequences and practical solutions"10mOptional: Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors10m

4 practice exercises

Beyond the Full F-test30mPlanned Comparisons: Defining Contrasts30mPlanned and Unplanned Comparisons30mType II Error and Power in the ANOVA Context30m

Week3

Week 3

9 hours to complete

Two-Way ANOVA and Interactions

9 hours to complete

7 videos (Total 79 min)

7 videos

Motivating the Two-way ANOVA Model10m The two-way ANOVA model9mThe Two-way ANOVA Model as a Regression Model9m Interaction Terms in the Two-way ANOVA Model: Definitions and Visualizations13m Interactions in the Two-way ANOVA Model: Formal Tests15m Two-way ANOVA Hypothesis Testing (no interaction)14mLooking Ahead: Two-Way ANOVA and Experimental Design5m

6 practice exercises

Motivating the Two-way ANOVA Model30mThe Two-way ANOVA Model30mThe Two-way ANOVA Model as a Regression Model30m Interaction Terms in the Two-way ANOVA Model: Definitions and Visualizations30m Interactions in the Two-way ANOVA Model: Formal Tests30m Two-way ANOVA Hypothesis Testing (no interaction)30m

Week4

Week 4

10 hours to complete

Experimental Design: Basic Concepts and Designs

10 hours to complete

7 videos (Total 79 min), 2 readings, 7 quizzes

7 videos

The Conceptual Framework of Experimental Design19mThe Completely Randomized Design12mThe Randomized Complete Block Design (RCBD)8mThe Randomized Complete Block Design (RCBD): Hypothesis Testing8mThe Factorial Design10m Further Issues in Experimental Design7mEthical Issues in Experimental Design12m

2 readings

Causation and Experimental Design10mResources on Ethics 10m

5 practice exercises

The Conceptual Framework of Experimental Design30m The Completely Randomized Design30mThe Randomized Complete Block Design (RCBD)30mThe Factorial Design30m Further Issues in Experimental Design30m

About the Statistical Modeling for Data Science Applications Specialization

Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.

This specialization 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. Logo adapted from photo by Vincent Ledvina on Unsplash

Statistical Modeling for Data Science Applications

Frequently Asked Questions

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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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ANOVA and Experimental Design

About this Course

Skills you will gain