# 2377 search results for "regression"

## Introduction to R for Data Science :: Session 7 [Multiple Linear Regression Model in R  + Categorical Predictors, Partial and Part Correlation]

June 9, 2016
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Welcome to Introduction to R for Data Science Session 7: Multiple Regression + Dummy Coding, Partial and Part Correlations [Multiple Linear Regression in R. Dummy coding: various ways to do it in R. Factors. Inspecting the multiple regression model: regression coefficients and their interpretation, confidence intervals, predictions. Introducing {lattice} plots + ggplot2. Assumptions: multicolinearity and testing it from the...

## Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

June 9, 2016
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Short form: Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time. Part 1: the proper preparation of data (including scaling) and use of principal components analysis (particularly for supervised learning or regression). Part 2: the introduction of y-aware scaling to direct the … Continue reading...

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

June 9, 2016
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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...

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

June 6, 2016
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Welcome to Introduction to R for Data Science Session 6: Linear Regression + EDA, and Normality tests The course is co-organized by Data...

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

June 2, 2016
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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...

## Principal Components Regression in R: Part 3

May 31, 2016
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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...

## Understanding beta binomial regression (using baseball statistics)

May 31, 2016
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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...

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

May 30, 2016
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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...

## 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

## End to end Logistic Regression in R

May 29, 2016
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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.