323 search results for "ANOVA"

Missing Value Treatment

April 25, 2016
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Missing Value Treatment

Missing values in data is a common phenomenon in real world problems. Knowing how to handle missing values effectively is a required step to reduce bias and to produce powerful models. Lets explore various options of how to deal with missing values and how to implement them. Data prep and pattern Lets use the BostonHousing Related Post

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Additive modelling global temperature time series: revisited

March 25, 2016
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Additive modelling global temperature time series: revisited

Quite some time ago, back in 2011, I wrote a post that used an additive model to fit a smooth trend to the then-current Hadley Centre/CRU global temperature time series data set. Since then the media and scientific papers have been full of reports of record warm temperatures in the past couple of years, of controversies (imagined) regarding...

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Football by the numbers

March 24, 2016
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Salvino A. Salvaggio In this blog I publish data analysis cases based on the R statistical language. No statistical or mathematical theory here, no discussions of the R language, no software tutorials, but only concrete case studies using existing R...

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R for Publication by Page Piccinini

March 23, 2016
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R for Publication by Page Piccinini

The goal of this course is to give you the skills to do the statistics that are in current published papers, and make pretty figures to show off your results. While we will go over the mathematical concepts behind the statistics, this is NOT meant to be a classical statistics class. We will focus more Lesson 0: Introduction...

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R 3.2.4 is released

March 11, 2016
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R 3.2.4 is released

R 3.2.4 (codename “Very Secure Dishes”) was released today. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below. Upgrading to R 3.2.4 on Windows If you are using Windows you can easily upgrade to the latest version of R using the installr … Continue reading...

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It’s not the p-values’ fault – reflections on the recent ASA statement (+relevant R resources)

March 10, 2016
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It’s not the p-values’ fault – reflections on the recent ASA statement (+relevant R resources)

Joint post by Yoav Benjamini and Tal Galili. The post highlights points raised by Yoav in his official response to the ASA statement (available as on page 4 in the ASA supplemental tab), as well as offers a list of relevant R resources. Summary The ASA statement about the misuses of the p-value singles it out. It is just … Continue reading...

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Titanic – Machine Learning from Disaster (Part 1)

February 17, 2016
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Titanic – Machine Learning from Disaster (Part 1)

Synopsis In the challenge Titanic – Machine Learning from Disaster from Kaggle, you need to predict of what kind of people were likely to survive the disaster or did not. In particular, they ask to apply the tools of machine learning to predict which passengers survived the tragedy. I’ve split

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Filling in the gaps – highly granular estimates of income and population for New Zealand from survey data

January 22, 2016
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Filling in the gaps – highly granular estimates of income and population for New Zealand from survey data

Individual-level estimates from survey data I was motivated by web apps like the British Office of National Statistics’ How well do you know your area? and How well does your job pay? to see if I could turn the New Zealand Income Survey into an individual-oriented estimate of income given age group, qualification, occupation, ethnicity, region and hours...

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R trends in 2015 (based on cranlogs)

January 20, 2016
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It is always fun to look back and reflect on the past year. Inspired by Christoph Safferling's post on top packages from published in 2015, I decided to have my own go at the top R trends of 2015. Contrary to Safferling's post I'll try to also (1) look at packages from previous years that hit the big...

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New Course! A hands-on introduction to statistics with R by A. Conway (Princeton University)

January 14, 2016
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New Course! A hands-on introduction to statistics with R by A. Conway (Princeton University)

The best way to learn is at your own pace. Combining the interactive R learning environment of DataCamp and the expertise of Prof. Conway of Princeton, we offer you an extensive online course on introductory statistics with R.  Start learning now...Whether you are a professional using statistics in your job,...

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