Regression is used primarily if you wish to predict or explain numeric data — This article is a continuation of series that last covered Bayes statistics. Volatility is inherent within the stock market, but perhaps the rise of “meme stocks” is something that is beyond traditional comprehension. This article is not about such “stonks” — which is the ironic misspelling of stocks that encapsulates…

Hard to believe there was once a controversy over probabilistic statistics — This article builds on my previous article about Bootstrap Resampling. Introduction to Bayes Models Bayesian models are a rich class of models, which can provide attractive alternatives to Frequentist models. Arguably the most well-known feature of Bayesian statistics is Bayes theorem, more on this later. With the recent advent of greater computational power and…

Simple, straightforward, convenient. — No, not Twitter Bootstrap — this bootstrapping is a way of sampling data, and it is one of the most important to consider what underlies the variation of numbers, the variation of distributions, what underlies distributions. To that end, bootstrapping works really, really well. …

“It’s tough to make predictions, especially about the future!” — Yogi Berra — Some wisdom transcends the ages! Introduction This article provides an overview of time series analysis. Time series are an extremely common data type. A quick Google search yields many applications, including: Demand forecasting: electricity production, traffic management, inventory management Medicine: Time-dependent treatment effects, EKG

Or how I learned to love the sigmoid “squishification” function for categorical data classification — This article is a brief continuation of my regression series. So far the regression examples I have been illustrating have been numeric, of numbers: predicting a continuous variable. With the Galton family height dataset, we were predicting children’s height — a continuously varying parameter. …

The continuing adventures of regularization and the eternal quest to prevent model overfitting! — This article is a continuation of last week’s intro to regularization with linear regression. Lettuce yonder back into the nitty-gritty of making the best data science/ machine learning models possible with more advanced techniques on simplifying our models. How do we simplify our models? By removing as many features as…

The one where we correct overfitting — This article is a continuation of my series on linear regression and bootstrap and Bayesian statistics. Previously I talked at length about linear regression, and now I am going to continue that topic. As I hinted at previously, I am going to bring up the topic of regularization. And what…

There were others who had forced their way to the top from the lowest rung by the aid of their bootstraps — This article builds on my Linear Regression and Bootstrap Resampling pieces. For the literary-minded among my readers, the subtitle is a quote from ‘Ulysses’ 1922, by James Joyce! The origin of the term “bootstrap” is in literature, though not from Joyce. The usage denotes: to better oneself by one’s own…

Oh boy, homoscedasticity — This article is a continuation of my previous one on Linear Regression. It is important to reiterate from my last article about the error formulae in least-squares regression.