Simple Example of TensorFlow

A good, detailed example of using TensorFlow for one of the most well known analytics techniques.  Instructive.

Linear Regression in Tensorflow  by Aaqib Saeed
Predicting house prices in Boston area

Tensorflow is an open source machine learning (ML) library from Google. It has particularly became popular because of the support for Deep Learning. Apart from that it's highly scalable and can run on Android. The documentation is well maintained and several tutorials available for different expertise levels. To learn more about downloading and installing Tensorflow, visit official website.  .... "

Comparing Standard error vs R Squared

Nicely done simple stat point made in this DSC post ....

Standard Error of the Regression vs. R-squared   by Jim Frostin in DSC

The standard error of the regression (S) and R-squared are two key goodness-of-fit measures for regression analysis. While R-squared is the most well-known amongst the goodness-of-fit statistics, I think it is a bit over-hyped.

In this post, I’ll compare these two statistics. We’ll also work through a regression example to help make the comparison. I think you’ll see that the oft overlooked standard error of the regression can tell you things that the high and mighty R-squared simply can’t. At the very least, you’ll find that the standard error of the regression is a great tool to add to your statistical toolkit!  .... "

Linear Regression Visual Geometry

Nice instructive piece in DSC.  Technical, but visually enlightening, about our most used analytical technique.   Good way to understand it and its components.   Posted by Jishnu Bhattacharya

A History of Data Mining

In Dataconomy.  Quite a few things left out based on my own experience.  A little too much emphasis paid to neural methods.    Patterns of all sort were being driven by statistics since regression and Bayesian.    Still a good reminder of where we have come from.

On Linear Regression

Good, non technical discussion.   Probably the most important method to really understand well.  Since it is most used.

A Crusade Against Multiple Regression Analysis

In the Edge
Yes, well known, but often ignored,  is all the context in the model?  Again in the realm of misusing statistics.  A lengthy conversation with Richard Nisbett.  A crusade, he says, that may be worth taking note of.

" .... The thing I’m most interested in right now has become a kind of crusade against correlational statistical analysis—in particular, what’s called multiple regression analysis. Say you want to find out whether taking Vitamin E is associated with lower prostate cancer risk. You look at the correlational evidence and indeed it turns out that men who take Vitamin E have lower risk for prostate cancer. Then someone says, "Well, let’s see if we do the actual experiment, what happens." And what happens when you do the experiment is that Vitamin E contributes to the likelihood of prostate cancer. How could there be differences? These happen a lot. The correlational—the observational—evidence tells you one thing, the experimental evidence tells you something completely different. ... " 

Linear vs Jackknife Regression

A technical and practical  piece on the topic.  I recall a project in the enterprise that could have used this explanation.  The idea is not used often enough, perhaps because it is not taught fundamentally in analytics overviews?  It should be near the top of the tool box.   Just sent this note out to several business intelligence practitioners to get comment.   Reminds me that there should always be a way to automate a half dozen common analyses at play.  And we are starting to see exactly that.   Again, surprised this has taken so long, it has been discussed for many years.

Regression using R and a Start-up Kit

Via Vincent Granville.  Good place to get started,  since even beginning data analysts know what regression is and how to use it with other packages, Like Excel.  So this is a good place to start with R.

Kinds of Regressions

Vincent Granville maps alternate regression types and their applications.  Nicely done.  It's very useful to know alternative tools and what they are good for.  After you know your business problem, the data needed and available, and have visualized it thoroughly, regression is the right next place to go.  See also his book, which is pointed to.  Making my way through it now.

Choosing Your Regression Variables

A nice simple topic that often comes up.  How exactly to you choose which variables to use when seeking to find a relationship using regression?  Such models are often as much about what we do not include, as about that which we do.  Choose wisely.  Also following this blog.

Spatial Analysis Techniques in R

What looks to be a good course in using the R statistics language for spatial analysis problems. " ... Topics covered include point pattern analysis, identifying clusters, measures of spatial association, geographically weighted regression and surface procession.... "