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.
Este curso faz parte do Programa de cursos integrados Statistical Modeling for Data Science Applications
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Programa de cursos integrados Statistical Modeling for Data Science ApplicationsUniversidade do Colorado em BoulderInformações sobre o curso
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Prazos flexíveis
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Certificados compartilháveis
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100% on-line
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Nível intermediário
Calculus, linear algebra, and probability theory.
Aprox. 39 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
Calculusand probability theory.Linear Algebra
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Calculus, linear algebra, and probability theory.
Aprox. 39 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade do Colorado em Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Comece a trabalhar rumo ao seu mestrado
Este curso é parte da graduação 100% on-line Master of Science in Data Science da Universidade do Colorado em Boulder.
Caso seja aceito para o programa completo, seus cursos contarão para sua graduação.
Programa - O que você aprenderá com este curso
11 horas para concluir
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.
11 horas para concluir
9 vídeos (Total 87 mín.)
9 horas para concluir
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.
9 horas para concluir
6 vídeos (Total 91 mín.), 2 leituras, 6 testes
9 horas para concluir
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.
9 horas para concluir
7 vídeos (Total 79 mín.)
10 horas para concluir
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.
10 horas para concluir
7 vídeos (Total 79 mín.), 2 leituras, 7 testes
Sobre Programa de cursos integrados Statistical Modeling for Data Science Applications
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.
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