Tag: statistics

Feed: Featured Blog Posts - Data Science Central. Author: ajit jaokar. This post is a part of my forthcoming book on Mathematical foundations of Data Science. In this post, we use the Perceptron algorithm to bridge the gap between high school maths and deep learning. Welcome comments Background As part of my role as course director of the Artificial Intelligence: Cloud and Edge Computing at the University..., I see more students who are familiar with programming than with mathematics. They have last learnt maths years ago at University. And then, suddenly they find that they encounter matrices, linear algebra etc ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Capri Granville. I stumbled upon this book by chance, when searching for material about time series (probably the most interesting chapter in this collection.) The various chapters are accessible from the top tabs, on this web page. It is mostly about R, but it has a few interesting chapters on statistical science too. Below is a summary. Time series decomposition (in chapter 23) This website was created with 6 major sections: Programming, Plotting, Regression, ANOVA, Advanced topics,and R-Apps.The tutorials build on each other, but can also be utilized independently from one ... Read More

Feed: R-bloggers. Author: Alastair Rushworth. Exploring categorical data with inspectdf What’s inspectdf and what’s it for? I often find myself viewing and reviewing dataframes throughout thecourse of an analysis, and a substantial amount of time can be spentrewriting the same code to do this. inspectdf is an R package designedto make common exploratory tools a bit more useful and easy to use. In particular, it’s very powerful be able to quickly see the contents ofcategorical features. In this article, we’ll summarise how to use theinspect_cat() function from inspectdf for summarising andvisualising categorical columns. First of all, you’ll need to have ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. ANOVA is routinely used in applied biology for data analyses, although, in some instances, the basic assumptions of normality and homoscedasticity of residuals do not hold. In those instances, most biologists would be inclined to adopt some sort of stabilising transformations (logarithm, square root, arcsin square root…), prior to ANOVA. Yes, there might be more advanced and elegant solutions, but stabilising transformations are suggested in most traditional biometry books, they are very straightforward to apply and they do not require any specific statistical software. I do not think ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.   Featured Resources and Technical Contributions  Assumptions of Linear Regression in One Picture + How to learn the maths of Data Science - using your high school maths knowledge Free Book: Statistics, Dataviz, and Data Cleaning with R  Everything You Wanted To Know About Recommender Systems  Simplified Logistic Regression  Alternatives to R-squared (with pluses and minuses)  Machine Learning : Unsupervised – ... Read More

Feed: R-bloggers. Author: hrbrmstr. I mentioned EtherCalc in a previous post and managed to scrounge some time to put together a fledgling {ethercalc} package (it’s also on GitLab, SourceHut, Bitbucket and GitUgh, just sub out the appropriate URL prefix). I’m creating a package-specific Docker image (there are a couple out there but I’m not supporting their use with the package as they have a CORS configuration that make EtherCalc API wrangling problematic) for EtherCalc but I would highly recommend that you just use it via the npm module. To do that you need a working Node.js installation and I highly ... Read More

Feed: R-bloggers. Author: Ian Johnson. I am going to use regression, decision trees, and the random forest algorithm to predict combined miles per gallon for all 2019 motor vehicles.  The raw data is located on the EPA government site After preliminary diagnostics, exploration and cleaning I am going to start with a multiple linear regression model. The variables/features I am using for the models are: Engine displacement (size), number of cylinders, transmission type, number of gears, air inspired method, regenerative braking type, battery capacity Ah, drivetrain, fuel type, cylinder deactivate, and variable valve.  There are 1253 vehicles in the dataset ... Read More

Feed: R-bloggers. Author: Niklas Junker. Networks are everywhere. We have social networks like Facebook, competitive product networks or various networks in an organisation. Also, for STATWORX it is a common task to unveil hidden structures and clusters in a network and visualize it for our customers.In the past, we used the tool Gephi to visualize our results in network analysis. Impressed by this outstanding pretty and interactive visualization, our idea was to find a way to do visualizations in the same quality directly in R and present it to our customers in an R Shiny app. Our first intention was ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Dr. Moloy De. Our client was an Emergency Response Management organization who handles medical, police and fire emergencies through the " 1-0-8 Emergency service". Currently the organization runs around 690 ambulances. An analysis is run on historic data extracted from client's management database. The live data input is fed to a simulation model to propose an optimal ambulance allocation providing an opportunity of cost reduction for the organization. In probability theory and statistics, the exponential distribution is the probability distribution that describes the time between events in a Poisson process, i.e ... Read More

Feed: R-bloggers. Author: R Views. We were recently presented with a problem where the decision maker wanted to understand how their data would naturally group together. The classic technique of k-means clustering was a natural choice; it’s well known, computationally efficient, and implemented in base R via the kmeans() function.Our problem has a slight wrinkle: the decision maker wished to see the data grouped with (nearly) equal sizes. Now, a ‘true’ statistician would tell the client that the right thing to do from a theoretical perspective was to use native k-means results because some centers can simply have more nearby ... Read More