1812 search results for "Excel"

New courses: Introduction to Statistics

January 31, 2017
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New courses: Introduction to Statistics

This week we are launching four new courses as part of our Introduction to Statistics curriculum. We are taking a modern approach to teaching statistics with the use of simulations and randomization rather than a more traditional theoretical one. We ha...

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On occasion of the 10,000th R package: The eoda Top 10

January 28, 2017
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On occasion of the 10,000th R package: The eoda Top 10

R just passed another milestone: 10,000 packages on CRAN. From A3 to zyp, from ABC analysis to zero-inflated models – 10,000 R packages mean great variety and methods for almost every use case. On occasion of this event we collected the top 10 R packages in collaboration with the ones who should know best: our …

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The Rt of naming your blog

January 28, 2017
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The Rt of naming your blog

In this post, I’m sharing a brand-new analysis! The reason for this is my blog being added to R-bloggers by Tal Galili after I filled this form. R-bloggers is a collection of blogs about R, whose new posts get added to the website via the magic of RS...

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The “Ten Simple Rules for Reproducible Computational Research” are easy to reach for R users

The “Ten Simple Rules for Reproducible Computational Research” are easy to reach for R users

“Ten Simple Rules for Reproducible Computational Research” is a freely available paper on PLOS computational biology. As I’m currently very interested on the subject of reproducible data analysis, I will these ten rules and the possible implem...

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January ’17 Tips and Tricks

January 27, 2017
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by Sean Lopp This month’s collection of Tips and Tricks comes from an excellent talk given at the 2017 RStudio::Conf in Orlando by RStudio Software Engineer Kevin Ushey. The slides from his talk are embedded below and cover features from autocompletion to R Markdown shortcuts. Use the left and right arrow keys to change slides.

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Visualization of MRI data in R

January 27, 2017
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Visualization of MRI data in R

Lately I was getting a little bored with genomic data (and then TCGA2STAT started to give me a segfault on my university’s high performance computing facility too :stuck_out_tongue:). So I decided to analyze some brain imaging data that I had lying a...

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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in R

January 26, 2017
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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in R

As I wrote in the previous post, I will continue in describing regression methods, which are suitable for double seasonal (or multi-seasonal) time series. In the previous post about Multiple Linear Regression, I showed how to use “simple” OLS regression method to model double seasonal time series of electricity consumption and use it for accurate forecasting. Interactions...

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Modelling extremes using generalized additive models

January 25, 2017
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Modelling extremes using generalized additive models

Quite some years ago, whilst working on the EU Sixth Framework project Euro-limpacs, I organized a workshop on statistical methods for analyzing time series data. One of the sessions was on the analysis of extremes, ably given by Paul Northrop (UCL Department of Statistical Science). That intro certainly whet my appetite but I never quite found the time to...

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Descriptive Analysis of MLST Data for MRSA

January 24, 2017
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Descriptive Analysis of MLST Data for MRSA

During one of my summers, I had the opportunity to conduct some research on the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in vulnerable populations and examining US emergency department data and I thought this would be a pretty interesting topic to expand on for my thesis in lieu of the increasing concerns of antimicrobial resistance, … Continue...

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Principal Component Analysis in R

January 23, 2017
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Principal Component Analysis in R

Principal component analysis (PCA) is routinely employed on a wide range of problems. From the detection of outliers to predictive modeling, PCA has the ability of projecting the observations described by variables into few orthogonal components defined at where the data ‘stretch’ the most, rendering a simplified overview. PCA is particularly powerful in dealing with multicollinearity and variables that … Continue...

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