Statistical modelling
Anyone moving from 'do these differ' to 'what predicts this, and by how much' — regression as it is actually reported.
3 modules from the Python for Data Analysis curriculum.
What you'll learn
By the end of this course
- Write the linear model y = β₀ + β₁x + ε and interpret the intercept and slope in real units
- Explain ordinary least squares (OLS) — fitting by minimising the sum of squared residuals
- Fit a model three ways — scipy.stats.linregress, numpy.polyfit, and statsmodels OLS — and read the statsmodels summary table
- Interpret R², coefficient standard errors, t-tests, p-values, and confidence intervals
- Produce predictions with the correct uncertainty: confidence intervals (for the mean) vs prediction intervals (for a new observation)
- Check the four regression assumptions (L·I·N·E) with residual diagnostic plots
- Recognise the traps: extrapolation, influential points, and reading causation into a fit
- Fit and interpret a multiple regression — coefficients as effects holding other predictors constant
Curriculum
3 sections · 3 lessons
Section 1
3 modules
Section 1
- Linear RegressionSection 5 taught you to compare groups and measure associations. Now we start modelling — building an equation that describes how one variable depends on another and lets us predict.~180 min
- Multiple RegressionSimple regression (Notebook 44) explains an outcome with one predictor. But tips depend on bill size and party size and mealtime; body mass depends on flipper length and species and sex.~180 min
- Logistic Regression (Statsmodels)Every model so far predicted a number (a tip, a body mass). But many of the most important questions are yes/no: will this customer churn?~180 min
About this course
3 modules from the Python for Data Analysis curriculum.
This course covers Linear Regression, Multiple Regression and Logistic Regression (Statsmodels).
About 9 hours of material across 3 modules, each with the dataset it teaches from and exercises with worked solutions.
Every module is a notebook you download and run yourself in JupyterLab — the point is that you execute the code, not that you watch someone else execute it. Recordings are added module by module; a module without one yet is taught in full by its notebook.
Who it's for
- Researchers and postgraduate students who need the analysis done properly, not just done
- Analysts moving from spreadsheets to something repeatable and auditable
- Anyone who has followed a Python tutorial and still cannot open their own data and get an answer
- Practitioners who can already run the basics and want Statistical modelling to a standard they can defend
What you'll need
- A computer you can install Python and JupyterLab on — notebook 02 walks you through it, on Windows and on macOS
- No prior programming experience for the foundations; the later tracks assume the earlier ones
- Comfort with the earlier tracks of Python for Data Analysis, or equivalent experience
- Your own dataset is welcome but not required — every module ships with the data it teaches from
Start Statistical modelling today
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