Data Visualization : The Indispensable Guide to Chart Design and Data Visualization [PART 1]... - JobLoving.com | Your Number One Source For daily Infographics & job opportunities
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Stanford - Statistical Learning | Ezequiel Aguilar-Gonzalez
About the course This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some …
How to Perform a Logistic Regression in R
Logistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume different values. […]
How to analyze Likert type dependent variables
Suppose your dependent variable (DV) is a Likert scale or something similar. That is, it’s some sort of rating, from 1 to 5 or 1 to 7 or some such. And suppose you want to regress that on several independent variables. What should you do? There are three broad categories of regression models that might be applicable. A lot of people routinely use linear regression (often simply called regression). Others routinely say this is incorrect, and that you should use ordinal logistic regression. A...