Author: drweb

When you’re building regression models with Python’s statsmodels library, you’ll quickly encounter add_constant. This function determines whether your model fits y = mx + b or just y = mx, which fundamentally changes how your model interprets data.I’ll walk you through what add_constant does, why you need it, and how to use it correctly in your statistical modeling work.Statsmodel Beginner’s Learning PathWhat Does add_constant Actually Do?The add_constant function adds a column of ones to your data array. That’s it at a mechanical level. But what this column of ones accomplishes is mathematically significant.When you run a linear regression, you’re estimating…

Read More

Do you know who managed to cut costs by a staggering 90% by abandoning microservices for a monolith in May 2023? Not a cash-strapped startup or an indie project—Amazon itself, for its Prime Video service. The same AWS that earns billions every year by selling microservices infrastructure admitted that, sometimes, a good old monolith wins.  This reversal from the company that practically wrote the playbook on distributed systems sent shockwaves through the cloud-native community. Amazon later removed the original blog post, but the internet never forgets, as you’ll see later. I’ve been speaking up against unnecessary or premature use of…

Read More

Statsmodels organizes its functionality into topic-based subpackages rather than dumping everything into a single namespace. Understanding this structure helps you find the right models quickly and import them efficiently.The library provides two primary access points: statsmodels.api for general use and statsmodels.formula.api for R-style formula syntax. Beyond these, specialized subpackages contain models, tools, and functions organized by statistical domain.Statsmodel Beginner’s Learning PathHow the API structure worksWhen you import statsmodels.api, you’re not loading the entire library. The API module collects the most commonly used classes and functions from various subpackages and presents them through a clean interface.Standard import convention: import statsmodels.api as…

Read More

When you first encounter Statsmodels code, you’ll likely run into two terms that seem designed to confuse: endog and exog. Most Python libraries use X and y for their variables. Scikit-learn does it. Pandas tutorials do it. Even introductory statistics courses use these letters. So why does Statsmodels break from convention with these strange terms?The answer reveals something fundamental about how Statsmodels thinks about statistical modeling, and understanding these terms early will save you from countless head-scratching moments later.Statsmodel Beginner’s Learning PathWhat endog and exog Actually MeanLet’s start with the definitions. In Statsmodels:endog stands for “endogenous variable”—this is your dependent…

Read More

In recent years, software development has made big strides. Tools like GitHub Copilot, ChatGPT, and other AI helpers have given developers the power to churn out code, tests, and documentation faster than ever. However, for solo founders and small startup teams, getting things written is only half the battle. Real challenges lie in getting it […]

Read More

Every tutorial you read shows a different way to import Statsmodels. One guide starts with import statsmodels.api as sm. Another uses from statsmodels.formula.api import ols. A third imports directly from submodules like from statsmodels.regression.linear_model import OLS. Which approach should you use?The confusion stems from a deliberate design choice. Statsmodels offers multiple import paths because different users need different things. Researchers writing academic papers want one workflow. Data scientists doing quick exploratory analysis want another. Understanding these three approaches will save you from blindly copying code that doesn’t match your actual needs.Statsmodel Beginner’s Learning PathUnderstanding the Three Import ApproachesApproach 1: statsmodels.api…

Read More