You’re running a regression on your sales data, and a few extreme values are throwing off your predictions. Maybe it’s a single huge order, or data entry errors, or legitimate edge cases you can’t just delete. Standard linear regression treats every point equally, which means those outliers pull your coefficients in the wrong direction. Robust Linear Models in statsmodels give you a better option.Statsmodel Beginner’s Learning PathWhat makes robust regression different from regular OLS?Ordinary least squares regression gives outliers disproportionate influence because errors are squared. An outlier with twice the typical error contributes four times as much to the loss…
Author: drweb
Linear mixed effects models solve a specific problem we’ve all encountered repeatedly in data analysis: what happens when your observations aren’t truly independent? I’m talking about situations where you have grouped or clustered data.Students nested within schools. Patients are visiting the same doctors. Multiple measurements from the same individuals over time.Standard linear regression assumes each data point is independent. Mixed effects models acknowledge that observations within the same group share something in common. I’ll walk you through how statsmodels handles these models and when you actually need them.Statsmodel Beginner’s Learning PathWhat Linear Mixed Effects Models Actually DoHere’s the core concept:…
You’ve collected data from the same patients over multiple visits, or tracked students within schools over several years. Your dataset has that nested, clustered structure where observations aren’t truly independent. Standard regression methods assume independence, but you know better. That’s where Generalized Estimating Equations (GEE) come in.GEE gives you a way to handle correlated data without making strict distributional assumptions. It’s designed for panel, cluster, or repeated measures data where observations may correlate within clusters but remain independent across clusters. Python’s statsmodels library implements GEE with a practical, straightforward API that lets you focus on your analysis rather than wrestling…
Alan reflects on a chaotic yet inspiring year in DevOps, highlighting the rise of AI in engineering, the maturation of DevSecOps, the evolution of hybrid work culture, the surge of platform engineering and IDPs, and the continued strength and inclusivity of the DevOps community — while acknowledging the talent crunch, tool sprawl and security theater that still challenge the industry.
Working with statsmodels feels great when everything runs smoothly.But we’ve all hit those frustrating moments when the library throws cryptic warnings, produces NaN values, or refuses to converge.After building dozens of statistical models with statsmodels, I’ve learned that most problems fall into predictable categories with straightforward solutions.Statsmodel Beginner’s Learning PathWhat causes multicollinearity errors and how do you resolve them?Statsmodels uses generalized inverse for linear models, which means cases of almost perfect multicollinearity or ill-conditioned design matrices might produce numerically unstable results. You’ll notice this when coefficients have wildly large standard errors or when signs flip unexpectedly between models.The Variance Inflation…
Vulnerability management in 2025 is overwhelmed by escalating CVEs and costly breaches; organizations must shift from slow, manual patching to continuous, risk-based, AI-powered remediation to stay secure.
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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…
Modern DevOps teams face outages driven by complex dependencies and AI-enabled systems; success now depends on moving from reactive monitoring to prescriptive, AI-assisted incident resolution that shortens MTTI and MTTR.
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…