Yes, I know, this has been tried a few times and no one listens.... At least not yet. Despite several studies showing otherwise, teams still punt more than they should. Admittedly, some of these studies have been less than rigorous, and often times, assumptions are made that warrant scrutiny (assuming a 50% success rate on all 4th down attempts for example). But I don't think it is the lack of scientific rigor that keeps change at bay. I think the failure to adopt a novel strategy has a lot…

ContinueAdded by Ray Hall on August 30, 2018 at 9:30am — No Comments

There are many good and sophisticated feature selection algorithms available in R. Feature selection refers to the machine learning case where we have a set of predictor variables for a given dependent variable, but we don’t know a-priori which predictors are most important and if a model can be improved by eliminating some predictors from a model. In linear regression, many students are taught to fit a data set to find the best model using so-called “least squares”. In most…

ContinueAdded by Blaine Bateman on April 30, 2018 at 7:30am — No Comments

Research fields usually follow the practice of categorizing continuous predictor variables, and they are the same who mostly use ANOVA. They often do it through median splits, the high value above the median and the low values below the median. However; this it seems is not that good an idea, and enlisted are some of the reasons to it:

- Median tends to vary from sample to sample. This makes the categories in various samples have various…

Added by Chirag Shivalker on October 24, 2017 at 10:00pm — No Comments

Linear Regression is one of the most widely used statistical models. If Y is a continuous variable i.e. can take decimal values, and is expected to have linear relation with X's variables, this relation could be modeled as linear regression, mostly the **first** model to fit,if we are planning to develop a model of forecasting Y or trying to build hypothesis about relation Xs on Y.

The…

ContinueAdded by Jishnu Bhattacharya on February 1, 2017 at 8:30pm — No Comments

Regressions are widely used to **estimate relations between variables or predict future values for a certain dataset**.

If you want to know how much of variable "x" interferes with…

ContinueAdded by Renata Ghisloti Duarte Souza Gra on December 27, 2016 at 10:00am — No Comments

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 Tesnorflow, visit official website.

To scratch the surface of this incredible ML library,…

ContinueAdded by Aqib Saeed on July 7, 2016 at 12:00pm — No Comments

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! …

ContinueAdded by LazyProgrammer.me on January 23, 2016 at 8:30pm — 2 Comments

For many scientists and data analysts, outliers are like a ‘black box’ in conventional statistics. Many believe that these outlier observations arise due to errors or due to improper procedures in the experiment. Majority of them eliminate the outliers unscientifically by brute force. Some identify them statistically but discard them as if they are junk. Some understand importance of the outliers but they do not know how to deal with it. If you are one among them or interested in scope of…

ContinueAdded by Venu Perla PhD on November 1, 2015 at 4:45pm — No Comments

Econometrics is fundamental to many of the problems that data scientists care about, and it requires many skills. There's philosophical skill, for thinking about whether fixed effects or random effects models are more appropriate, for example, or what the direction of causality in a particular problem is. There's some coding, including knowing the right commands to interact with statistical programs like Stata or R, and how to interpret their output. There's the intuition to know which…

ContinueAdded by Bradford Tuckfield on October 1, 2015 at 2:30pm — 1 Comment

I have never been formally trained on how to deal with seasonality. But I wanted to take a moment to share my perspective based on experience, which I hope readers will find fairly straightforward. Some people use sales revenues in order to evaluate seasonal differences. I find it more desirable to analyze units sold if possible. A price increase resulting in slightly higher revenues does not in itself represent increased demand. Nor should discounted prices leading to reduced revenues…

ContinueAdded by Don Philip Faithful on August 23, 2015 at 5:19am — No Comments

- Another Analysis of Punting on 4th Down
- Simple automated feature selection using lm() in R
- When to Categorize Continuous Predictor in a Regression Model?
- Linear Regression Geometry
- Getting Started with Regression in R
- Linear Regression in Tensorflow
- Step-by-step video courses for Deep Learning and Machine Learning

- Step-by-step video courses for Deep Learning and Machine Learning
- Linear Regression in Tensorflow
- Linear Regression Geometry
- Getting Started with Regression in R
- Simple automated feature selection using lm() in R
- When to Categorize Continuous Predictor in a Regression Model?
- Linear Algebra Formulas for Econometrics

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