The Econometrics of Temporal Aggregation – VI – Tests of Linear Restrictions
Many of the statistical tests that we perform routinely in econometrics can be affected by the level of aggregation of the data. Here, Let’s focus on time-series data, and with temporal aggregation. I’m going to show you some preliminary results from work that I have in progress with Ryan Godwin. These results relate to one particular test, but work covers a variety of testing problems.

Quick Guide: Steps To Perform Text Data Cleaning in Python
Below is the infographic, which displays the steps of cleaning this data related to tweets before mining them. While the example in use is of Twitter, you can of course apply these methods to any text mining problem. We’ve used Python to execute these cleaning steps.

10+ Best AngularJS Tutorials Useful for Developers
AngularJS is a powerful JavaScript framework which is used in SPA (Single Page Application) projects. It expands HTML DOM with added attributes and makes it more responsive. The framework is widely used by a number of developers which is open source and comes completely free. Generally people use the AngularJS docs to learn the framework but it is not enough for beginners unless they study a proper tutorial about it. This is why we have mentioned below a few but popular AngularJS tutorials which will help you learn it.

scikit-learn video #7: Optimizing your model with cross-validation
In this video, we’ll focus on K-fold cross-validation, an incredibly popular (and powerful) machine learning technique for model evaluation. If you’ve spent any time in the Kaggle forums, you know that experienced Kagglers talk frequently about the importance of validating your models locally to avoid overfitting the public leaderboard, and cross-validation is usually the validation method of choice!

Generalized Linear Mixed Models: the FAQ
The most commonly used functions for mixed modeling in R are
• linear mixed models: aov(), nlme::lme, lme4::lmer;
• generalized linear mixed models (GLMMs): MASS::glmmPQL, lme4::glmer, MCMCglmm::MCMCglmm;
• nonlinear mixed models: nlme::nlme, lme4::nlmer.

The “R Consortium”
The R Consortium, Inc. is a group of businesses organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software.
The R language is an open source environment for statistical computing and graphics, and runs on a wide variety of computing platforms. The R language has enjoyed significant growth, and now supports over 2 million users. A broad range of industries have adopted the R language, including biotech, finance, research and high technology industries. The R language is often integrated into third party analysis, visualization and reporting applications.
The central mission of the R Consortium is to work with and provide support to the R Foundation and to the key organizations developing, maintaining, distributing and using R software through the identification, development and implementation of infrastructure projects.
From a governance perspective, the business of the consortium is managed by a Board of Directors. The technical aspects of the project, including the development and implementation of infrastructure projects, is overseen by an Infrastructure Steering Committee. While the initial members of the Infrastructure Steering Committee consist of representatives of the founding members of the R Consortium, Inc., project leads of key infrastructure projects will become voting members of the Infrastructure Steering Committee.
Potential infrastructure projects include:
• strengthening the R Forge infrastructure;
• assisting the Stanford University group running user!R 2016;
• developing documentation; and
• encouraging increased communication and collaboration among users and developers of the R language.