2262 search results for "regression"

R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

June 9, 2016
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R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

Today we’ll be moving from linear regression to logistic regression. This lesson also introduces a lot of new dplyr verbs for data cleaning and summarizing that we haven’t used before. Once again, I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go Lesson 3: Logistic...

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Introduction to R for Data Science :: Session 6 [Linear Regression Model in R  + EDA, and Normality Tests]

June 6, 2016
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Introduction to R for Data Science :: Session 6 [Linear Regression Model in R  + EDA, and Normality Tests]

Welcome to Introduction to R for Data Science Session 6: Linear Regression + EDA, and Normality tests The course is co-organized by Data...

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R for Publication by Page Piccinini: Lesson 2 – Linear Regression

June 2, 2016
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R for Publication by Page Piccinini: Lesson 2 – Linear Regression

This is our first lesson where we actually learn and use a new statistic in R. For today’s lesson we’ll be focusing on linear regression. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go back and do it. By the Lesson 2: Linear...

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Principal Components Regression in R: Part 3

May 31, 2016
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Principal Components Regression in R: Part 3

by John Mount Ph. D. Data Scientist at Win-Vector LLC In her series on principal components analysis for regression in R, Win-Vector LLC's Dr. Nina Zumel broke the demonstration down into the following pieces: Part 1: the proper preparation of data and use of principal components analysis (particularly for supervised learning or regression). Part 2: the introduction of y-aware...

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Understanding beta binomial regression (using baseball statistics)

May 31, 2016
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Understanding beta binomial regression (using baseball statistics)

Previously in this series: Understanding the beta distribution Understanding empirical Bayes estimation Understanding credible intervals Understanding the Bayesian approach to false discovery rates Understanding Bayesian A/B testing In this series we’ve been using the empirical Bayes method to estimate batting averages of baseball players. Empirical Bayes is useful here because when we...

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Principal Components Regression, Pt. 3: Picking the Number of Components

May 30, 2016
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Principal Components Regression, Pt. 3: Picking the Number of Components

In our previous note we demonstrated Y-Aware PCA and other y-aware approaches to dimensionality reduction in a predictive modeling context, specifically Principal Components Regression (PCR). For our examples, we selected the appropriate number of principal components by eye. In this note, we will look at ways to select the appropriate number of principal components in … Continue reading...

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Visualizing Bootrapped Stepwise Regression in R using Plotly

May 29, 2016
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We all have used stepwise regression at some point. Stepwise regression is known to be sensitive to initial inputs. One way to mitigate this sensitivity is to repeatedly run stepwise regression on bootstrap samples. R has a nice package called bootStepAIC() which (from its description) “Implements a Bootstrap procedure to investigate the variability of model

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End to end Logistic Regression in R

May 29, 2016
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End to end Logistic Regression in R

Logistic regression, or logit regression is a regression model where the dependent variable is categorical. I have provided code below to perform end-to-end logistic regression in R including data preprocessing, training and evaluation. The dataset used can be downloaded from here.

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Principal Components Regression in R: Part 2

May 24, 2016
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Principal Components Regression in R: Part 2

by John Mount Ph. D. Data Scientist at Win-Vector LLC In part 2 of her series on Principal Components Regression Dr. Nina Zumel illustrates so-called y-aware techniques. These often neglected methods use the fact that for predictive modeling problems we know the dependent variable, outcome or y, so we can use this during data preparation in addition to using...

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Principal Components Regression, Pt. 2: Y-Aware Methods

May 23, 2016
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Principal Components Regression, Pt. 2: Y-Aware Methods

In our previous note, we discussed some problems that can arise when using standard principal components analysis (specifically, principal components regression) to model the relationship between independent (x) and dependent (y) variables. In this note, we present some dimensionality reduction techniques that alleviate some of those problems, in particular what we call Y-Aware Principal Components … Continue reading...

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