Linear Models

 

 


 

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E.g., 2016-12-07
E.g., 2016-12-07
E.g., 2016-12-07
Dec 12th 2016

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Average: 8 (5 votes)
Dec 12th 2016

How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise.

Average: 9 (3 votes)
Dec 5th 2016

Este curso forma parte de una secuencia con la que se propone un acercamiento a la Matemática Preuniversitaria que prepara para la Matemática Universitaria. En él se asocia un significado real con el contenido matemático que se aprende y se integran tecnologías digitales en el proceso de aprendizaje.

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Dec 5th 2016

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective.

Average: 8 (1 vote)
Nov 28th 2016

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective.

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Nov 15th 2015

Learn to use R programming to apply linear models to analyze data in life sciences.

Average: 4.7 (3 votes)