Do I need to install anything?

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For K-12 Students · Causal Inference

Causal Inference for an outlier scan

Built for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework. Simulate an outlier scan live below — adjust the inputs and watch it respond, right in your browser.

Linear Regression

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Linear RegressionLive

least squares & R²

Controls

Presets

True slope

Noise level

Data points

Ordinary least squares finds the line that minimizes the sum of squared vertical residuals (the gray drop-lines). R² measures the fraction of variance the line explains — 1 is perfect, 0 is useless. Add noise or remove points to watch the fit and R² degrade.

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Data Inspector

Fitted slope0.000

Intercept0.000

R²0.000

Correlation r0.000

Governing equation

\overset{y}{^} = 0.00 + 0.00 x, β_{1} = \frac{\sum ( x - x ˉ ) ( y - y ˉ )}{\sum ( x - x ˉ ) ^{2}}, R^{2} = 0.00

Reading this result: R² = 0.00: noise (2) swamps the signal, so least squares can barely tell the slope from flat — adding data points would tighten the estimate.

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Hypothesis Test

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Hypothesis Test (z-test)Live

p-values & rejection regions

Controls

Presets

Sample mean x̄

Null mean μ₀

Std deviation s

Sample size n

Two-tailed test

A hypothesis test asks whether a sample mean is far enough from the null value to be surprising by chance alone. The test statistic z measures that distance in standard errors; if it falls in the red rejection region (p below 0.05) we reject the null. Larger samples shrink the standard error and sharpen the test.

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Data Inspector

Standard error1.265

z-statistic1.581

p-value0.1138

Decisionfail to reject

Governing equation

z = \frac{x ˉ - μ _{0}}{σ / n} = \frac{52 - 50}{8/ 40} = 1.58

Reading this result: The statistic sits in the white central zone, so the gap between x̄ and μ₀ is within ordinary sampling noise and you fail to reject H₀.

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Frequently asked questions

Is this good for k-12 students?: Yes — this version of "Causal Inference for an outlier scan" is framed for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.