# 1925 search results for "regression"

## How to perform a Logistic Regression in R

September 13, 2015
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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.

## Fitting Polynomial Regression in R

September 10, 2015
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A linear relationship between two variables x and y is one of the most common, effective and easy assumptions to make when trying to figure out their relationship. Sometimes however, the true underlying relationship is more complex than that, and this is when polynomial regression comes in to help. Let see an example from economics:

## Logistic Regression in R – Part Two

September 2, 2015
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$Logistic Regression in R – Part Two$

My previous post covered the basics of logistic regression. We must now examine the model to understand how well it fits the data and generalizes to other observations. The evaluation process involves the assessment of three distinct areas – goodness of fit, tests of individual predictors, and validation of predicted values – in order to

## Predicting creditability using logistic regression in R (part 1)

September 2, 2015
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As I said in the previous post, this summer I’ve been learning some of the most popular machine learning algorithms and trying to apply what I’ve learned to real world scenarios. The German Credit dataset provided by the UCI Machine Learning Repository is another great example of application.The German Credit dataset contains 1000 samples of applicants asking for...

## Logistic Regression in R – Part One

September 1, 2015
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$Logistic Regression in R – Part One$

Please note that an earlier version of this post had to be retracted because it contained some content which was generated at work. I have since chosen to rewrite the document in a series of posts. Please recognize that this may take some time. Apologies for any inconvenience.   Logistic regression is used to analyze the

## Bayesian regression models using Stan in R

September 1, 2015
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It seems the summer is coming to end in London, so I shall take a final look at my ice cream data that I have been playing around with to predict sales statistics based on temperature for the last couple of weeks , , .Here I will use the new brms (GitHub, CRAN) package...

## Kickin’ it with elastic net regression

With the kind of data that I usually work with, overfitting regression models can be a huge problem if I'm not careful. Ridge regression is a really effective technique for thwarting overfitting. It does this by penalizing the L2 norm… Continue reading →

## Evaluating Logistic Regression Models

August 17, 2015
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Logistic regression is a technique that is well suited for examining the relationship between a categorical response variable and one or more categorical or continuous predictor variables. The model is generally presented in the following format, where β refers to the parameters and x represents the independent variables. log(odds)=β0+β1∗x1+...+βn∗xn The log(odds), or log-odds ratio, is defined

## R, Python, and SAS: Getting Started with Linear Regression

August 16, 2015
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Consider the linear regression model, \$\$ y_i=f_i(boldsymbol{x}|boldsymbol{beta})+varepsilon_i, \$\$ where \$y_i\$ is the response or the dependent variable at the \$i\$th case, \$i=1,cdots, N\$ and the predictor or the independent variable is the \$boldsymbol{x}\$ term defined in the mean function \$f_i(boldsymbol{x}|boldsymbol{beta})\$. For simplicity, consider the following simple linear regression (SLR) model, \$\$ y_i=beta_0+beta_1x_i+varepsilon_i. \$\$ To obtain the (best) estimate...

## Bivariate Linear Regression

August 13, 2015
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Regression is one of the – maybe even the single most important fundamental tool for statistical analysis in quite a large number of research areas. It forms the basis of many of the fancy statistical methods currently en vogue in the social sciences. Multilevel analysis and structural equation modeling are perhaps the most widespread and