A guided reading path
Data analysis: from raw observations to evidence
Know what each row measures, explore the data and separate what your sample shows from what you can infer about a wider population.
01 · Understand the question
Build the foundations
Read in this order, or open the question you are working on.
What is data analysis? A practical introduction with examples
Data analysis turns observations into answers. Learn the steps, common types of analysis and how to turn a question into a defensible conclusion.
Exploratory data analysis: what to check before fitting a model
Exploratory data analysis checks a dataset's structure, missing values and unusual observations. Follow a practical checklist before choosing a model.
Descriptive vs inferential statistics: the difference with examples
Descriptive statistics summarise observed data; inference makes claims beyond them. Compare examples, confidence intervals and the assumptions behind each.
02 · Examine the assumptions
Explore methods when you need them
A clear description may already answer your question. If modelling is useful, these references explain the estimator, supported options, a runnable example and limitations. Choose one for your question and assumptions.
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Linear models and IV
OLS regression in Python with robust and clustered standard errors
Fit OLS and weighted least squares in Python with HC0–HC3, clustered or HAC standard errors, then run tests, linear combinations and margins.
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Linear models and IV
Quantile regression in Python: qreg, bootstrap and simultaneous quantiles
Estimate median and other conditional quantiles in Python with qreg, bsqreg and sqreg, and test whether a slope differs across the distribution.
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Tests and inference
Multiple testing correction in Python: Holm, BH and Romano–Wolf
Adjust p-values for multiple hypotheses in Python with Bonferroni, Holm, Benjamini–Hochberg or the Romano–Wolf stepdown, and choose between FWER and FDR.
03 · Work through the evidence
Try a worked lab
These exercises use synthetic teaching data. The full handout, Python analysis and available resources are linked on each lab page.
Worked lab
Define the unit of observation
Work through measurement scales, units and the questions a dataset can answer.
Worked lab
Compare means and medians
See how distribution shape changes the interpretation of a summary statistic.
Worked lab
Move from a sample to an interval
Explore sampling uncertainty and the interpretation of confidence intervals.
Try it on your own data
OpenEconometrics is free and open source under Apache 2.0. Install the Python library or download the desktop alpha.
pip install openecon