# 2223 search results for "Regression"

## Introduction to Logistic Regression with R

October 6, 2015
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In my previous blog I have explained about linear regression. In today’s post I will explain about logistic regression.         Consider a scenario where we need to predict a medical condition of a patient (HBP) ,HAVE HIGH BP or NO HIGH BP, based on some observed symptoms – Age, weight, Issmoking, Systolic...

## Using Linear Regression to Predict Energy Output of a Power Plant

September 29, 2015
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In this article, I will show you how to fit a linear regression to predict the energy output at a Combined Cycle Power Plant(CCPP). The dataset is obtained from the UCI Machine Learning Repository. The dataset contains five columns, namely, Ambient Temperature (AT), Ambient Pressure (AP), Relative Humidity (RH), Exhaust Vacuum (EV), and net hourly

## Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

September 15, 2015
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Now that we fitted the classifier and run some preliminary tests, in order to get a grasp at how our model is doing when predicting creditability we need to run some cross validation methods.Cross validation is a model evaluation method that does not use conventional fitting measures (such as R^2 of linear regression) when trying to evaluate the model....

## Deming and Passing Bablok Regression in R

Regression Methods In this post we will be discussing how to perform Passing Bablok and Deming regression in R. Those who work in Clinical Chemistry know that these two approaches are required by the journals in the field. The idiosyncratic affection for these two forms of regression appears to be historical but this is something … Continue reading...

## 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...