# 2374 search results for "regression"

## Estimate Regression with (Type-I) Pareto Response

December 11, 2016
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The Type-I Pareto distribution has a probability function shown as below f(y; a, k) = k * (a ^ k) / (y ^ (k + 1)) In the formulation, the scale parameter 0 < a < y and the shape parameter k > 1 . The positive lower bound of Type-I Pareto distribution is particularly

## Forecast double seasonal time series with multiple linear regression in R

December 2, 2016
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I will continue in describing forecast methods, which are suitable to seasonal (or multi-seasonal) time series. In the previous post smart meter data of electricity consumption were introduced and a forecast method using similar day approach was proposed. ARIMA and exponential smoothing (common methods of time series analysis) were used as forecast methods. The biggest disadvantage of this...

## Create, Interpret, and Use a Linear Regression Model in R

November 29, 2016
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In my last post, we looked at how to create a correlation matrix in R. Specifically, we used data pulled from the web to see which variables were most highly correlated with an automobile’s fuel economy. Suppose, however, that we are trying to guess the fuel economy of a new car without actually having driven

## RStanARM basics: visualizing uncertainty in linear regression

November 18, 2016
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As part of my tutorial talk on RStanARM, I presented some examples of how to visualize the uncertainty in Bayesian linear regression models. This post is an expanded demonstration of the approaches I presented in that tutorial. Data: Does brain mass predict how much mammals sleep in a day? Let’s use the mammal sleep dataset from ggplot2. This dataset contains the number of...

## Curve Fitting or Polynomial Regression between two variables

November 15, 2016
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When the relation between two variables x and y is not linear and if there exists a curvilinear relationship (which can be observed by means of a scatter plot between x and y), then one can perform curve fitting or polynomial regression between these two variables. To know the details as to how to perform … Continue...

## LR02: SD line, GoA, Regression

This posts continues the discussion of correlation started on LR01: Correlation. We will try to answer the following questions: Should correlation be used for any pair of data? Does association mean causation? What are ecological correlations? What hap...

## glmnetUtils: quality of life enhancements for elastic net regression with glmnet

November 1, 2016
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The glmnetUtils package provides a collection of tools to streamline the process of fitting elastic net models with glmnet. I wrote the package after a couple of projects where I found myself writing the same boilerplate code to convert a data frame into a predictor matrix and a response vector. In addition to providing a formula interface, it also...

## The Bayesian approach to ridge regression

In a previous post, we demonstrated that ridge regression (a form of regularized linear regression that attempts to shrink the beta coefficients toward zero) can be super-effective at combating overfitting and lead to a greatly more generalizable model. This approach… Continue reading →

## Cut your regression to mediocrity with this 1 WEIRD TRICK!

October 27, 2016
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Well, like all clickbait articles, this isn’t going to be nearly as helpful to you as you were hoping — at least, not if you’re looking for some way to stop living an average life and fulfill your potential and blah blah blah. But, if you’re concerned about regression to the mean (aka “regression to… Continue reading...

## Binomial Logistic Regression.

October 2, 2016
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I’m officially a Kaggler. Cut to the good ol’ Titanic challenge. Ol’ is right – It’s been running since 2012 and ends in 3 months! I showed up late to the party. Oh well, I guess it’s full steam ahead from now on. The competition  ‘Machine Learning from Disaster’ asks you to apply machine learning to analyse and…