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All Models Are Wrong

This article is by Sydney Firmin and originally appeared on the Alteryx Data Science Blog here:

All Models Are Wrong
The three broad categories of assumptions made by statistical models are distributional assumptions (assumptions about the distribution of values in a variable or the distribution of observational errors), structural assumptions (assumptions about the functional relationship between variables), and cross-variation assumptions (joint probability distribution).

For example, a linear regression model assumes that the relationships between variables in a data set are linear (and only linear). In the eyes of a linear model, any distance between the observations that make up the data set and the modeled line is just noise (i.e., random or unexplained fluctuations in the data) and can ultimately be ignored.

Qlik February 2019 – New Feature Releases for Qlik Sense, NPrinting and Qlik Sense Mobile

New updates for Qlik February 2019 is now available for Qlik Sense, Qlik Sense Mobile and NPrinting.

Qlik Sense February 2019 - the latest feature release for Qlik Sense.  New features including:

Single Page Application Flow
Dollar-sign expansion preview
The Visualization Bundle
Plus a number of bug fixes

Qlik NPrinting February 2019 - the latest feature release for NPrinting.

Alteryx Analytics 2019.1

Alteryx Analytics 2019.1 is now released!

The major highlights in this release for analysts and data scientists are multi-select caching, additional charting options, and new Python Tool capabilities. For our IT and admins, we’ve dramatically simplified the admin experience with easier navigation in Server and model deployment scaling in Promote.