Scatterize 3

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Open a dataset

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Drop a CSV anywhere on the page · Data never leaves your browser

Examples: Anscombe's Quartet · Motor Trends '74 Cars · Ames Housing

Diagnostics

About Scatterize

You should look at your data. Examining your data outside of the context of summary statistics is an essential part of the scientific process. Scatterize is designed to help you understand the distribution of your data and let you see how different choices about model calculation, outlying points, and covariates may affect your results.

Statistical models

OLS
Standard linear regression. Fits a line minimizing the sum of squared errors. Sensitive to outliers.
Robust
IRLS regression with Tukey biweight weighting. Automatically downweights outliers.
Spearman
Rank-based correlation. Measures monotonic association without assuming linearity or normality.
Theil-Sen
Median-based slope estimator. Highly robust to outliers; uses the median of all pairwise slopes.

Smoothers

Running median
Local medians in a sliding window. Robust to outliers; reveals trends without assuming a shape.
LOWESS
Locally weighted regression. A smooth curve fitted independently at each point.

Censoring

Click any point to censor it — exclude it from the regression while keeping it visible as a grey hollow marker. Click again to restore it. Use censoring to explore how individual observations affect your results.

Privacy & sharing

Everything runs in your browser — your data is never sent to a server. If you load a public CSV or Google Sheet URL, all settings (variables, model, censored points) are encoded in the URL. Share that URL and anyone with access to the data will see the same view.

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Keyboard shortcuts

j / kNext / previous Y variable
u / iPrevious / next X variable
o / pPrevious / next model
l / ;Next / previous legend group
cClear censored points
?Show / hide this help

Point navigation

← / →Previous / next point
⌥ ← / →Back / forward 1 percentile
⌘ ← / →First / last point
SpaceCensor / uncensor selected point
n / mNext / previous navigation criterion

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