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.
Ce cours fait partie de la Spécialisation Statistical Modeling for Data Science Applications
Offert par
Spécialisation Statistical Modeling for Data Science ApplicationsUniversité du Colorado à BoulderÀ propos de ce cours
15 640 consultations récentes
Dates limites flexibles
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Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
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Cours 2 sur 3 dans le
Niveau intermédiaire
Calculus, linear algebra, and probability theory.
Approx. 40 heures pour terminer
Anglais
Sous-titres : Anglais
Compétences que vous acquerrez
- Calculus
- and probability theory.
- Linear Algebra
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 3 dans le
Niveau intermédiaire
Calculus, linear algebra, and probability theory.
Approx. 40 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université du Colorado à 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.
Commencez à travailler pour obtenir votre master
Ce cours fait partie du diplôme intégralement en ligne Master of Science in Data Science de Université du Colorado à Boulder.
Si vous êtes admis(e) au programme complet, vos cours sont pris en compte dans votre apprentissage diplômant.
Programme de cours : ce que vous apprendrez dans ce cours
11 heures pour terminer
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 heures pour terminer
9 vidéos (Total 87 min), 2 lectures, 12 quiz
9 heures pour terminer
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 heures pour terminer
6 vidéos (Total 91 min), 2 lectures, 6 quiz
9 heures pour terminer
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 heures pour terminer
7 vidéos (Total 79 min)
10 heures pour terminer
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 heures pour terminer
7 vidéos (Total 79 min), 2 lectures, 7 quiz
À propos du Spécialisation 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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