2130 search results for "Regression"

Using segmented regression to analyse world record running times

December 30, 2015
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Using segmented regression to analyse world record running times

by Andrie de Vries A week ago my high school friend, @XLRunner, sent me a link to the article "How Zach Bitter Ran 100 Miles in Less Than 12 Hours". Zach's effort was rewarded with the American record for the 100 mile event. Zach Bitter holds the American record for the 100 mile This reminded me of some analysis...

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Regression Diagnostic Plots using R and Plotly

December 25, 2015
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Regression Diagnostic Plots using R and Plotly

Plotly is a platform for making, editing, and sharing customizable and interactive graphs. Embedding Plotly graphs in a R-Markdown document is very easy. Here, we will genarate a R-Markdown document with embedded Plotly charts to visualize regression diagnostic plots similar to the ones generated by using plot() on a fitted lm() object. R-Studio First step

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Prediction Intervals for Poisson Regression

December 20, 2015
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Prediction Intervals for Poisson Regression

Different from the confidence interval that is to address the uncertainty related to the conditional mean, the prediction interval is to accommodate the additional uncertainty associated with prediction errors. As a result, the prediction interval is always wider than the confidence interval in a regression model. In the context of risk modeling, the prediction interval

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Fitting Generalized Regression Neural Network with Python

December 9, 2015
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Fitting Generalized Regression Neural Network with Python

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RRegrs: exploring the space of possible regression models

November 22, 2015
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RRegrs: exploring the space of possible regression models

Machine learning is a field of science that focusses on mathematically describing patterns in data. Chemometrics does this for chemical data. Examples are (nano)QSAR where structural information is related to biological activity. I studied during my Ph...

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Correlation and Linear Regression

November 14, 2015
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Correlation and Linear Regression

Before going into complex model building, looking at data relation is a sensible step to understand how your different variable interact together. Correlation look at trends shared between two variables, and regression look at causal relation between a predictor (independent variable) and a response (dependent) variable. Correlation As mentioned above correlation look at global movement

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Applied Statistical Theory: Quantile Regression

November 13, 2015
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Applied Statistical Theory: Quantile Regression

This is part two of the ‘applied statistical theory’ series that will cover the bare essentials of various statistical techniques. As analysts, we need to know enough about what we’re doing to be dangerous and explain approaches to others. It’s not enough to say “I used X because the misclassification rate was low.” Standard linear

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Estimating Quasi-Poisson Regression with GLIMMIX in SAS

October 14, 2015
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Estimating Quasi-Poisson Regression with GLIMMIX in SAS

When modeling the frequency measure in the operational risk with regressions, most modelers often prefer Poisson or Negative Binomial regressions as best practices in the industry. However, as an alternative approach, Quasi-Poisson regression provides a more flexible model estimation routine with at least two benefits. First of all, Quasi-Poisson regression is able to address both

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Estimating Quasi-Poisson Regression with GLIMMIX in SAS

October 14, 2015
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Estimating Quasi-Poisson Regression with GLIMMIX in SAS

When modeling the frequency measure in the operational risk with regressions, most modelers often prefer Poisson or Negative Binomial regressions as best practices in the industry. However, as an alternative approach, Quasi-Poisson regression provides a more flexible model estimation routine with at least two benefits. First of all, Quasi-Poisson regression is able to address both

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Plotting regression curves with confidence intervals for LM, GLM and GLMM in R

October 8, 2015
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Plotting regression curves with confidence intervals for LM, GLM and GLMM in R

Once models have been fitted and checked and re-checked comes the time to interpret them. The easiest way to do so is to plot the response variable versus the explanatory variables (I call them predictors) adding to this plot the fitted regression curve together (if you are feeling fancy) with a confidence interval around it.

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