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Quality Control (SPC) for a lottery-odds check

Simulate a lottery-odds check live in your browser. This runs the real Quality Control (SPC) solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

1

Control Chart

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SPC Control ChartLive

Controls

Presets

Statistical process control watches a process over time against control limits set at three standard deviations from the target. Points inside are normal random variation — leave them alone. A point beyond the limits, or a run trending one way, signals a real change worth investigating. Nudge the process shift and watch points breach the limits.

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Data Inspector

Center line50
Control limits±3σ (44–56)
Out of control0

Governing equation

Reading this result: The process is centered on target: points scatter randomly inside the ±3σ limits, so every point is just noise — reacting now would only add variation (tampering).

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

2

Process Capability

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Process Capability (Cp / Cpk)Live

Controls

Presets

Cp compares the spec width to the process spread — can the process fit inside the tolerances at all? Cpk also accounts for how well-centered it is. A Cpk of 1.33 is the usual bar for capable; 2.0 is Six Sigma quality with just 3.4 defects per million. Widen the spread or push the mean off-center and defects climb fast.

▶ Run in Python

Data Inspector

Cp1.00
Cpk1.00
Defects2700 ppm
Ratingmarginal
Sigma level4.5σ

Capability indices

Reading this result: Centered and capable at Cpk 1.00, comfortably above the 1.33 industry bar.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

3

Hypothesis Test

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Hypothesis Test (z-test)Live

Controls

Presets

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.

▶ Run in Python

Data Inspector

Standard error1.265
z-statistic1.581
p-value0.1138
Decisionfail to reject

Governing equation

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.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Quality Control (SPC) tool models a lottery-odds check with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Quality Control (SPC)

Other ways to simulate a lottery-odds check

Frequently asked questions

How do I simulate a lottery-odds check?
Open this page and use the live Quality Control (SPC) tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?
Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?
Absolutely — every input is adjustable, and with data import you can drive a lottery-odds check from your own measurements.