Examples
Examples
Try the starting example
- Ordered-category square contingency counts JSON
- [[30,5,2],[4,25,3],[1,2,28]]
- Quadratic weighted kappa
- 0.808486
- Observed weighted disagreement
- 0.065
- Expected weighted disagreement
- 0.3394
Try another scenario
- Ordered-category square contingency counts JSON
- [[31,5,2],[4,25,3],[1,2,28]]
- Quadratic weighted kappa
- 0.811025
- Observed weighted disagreement
- 0.064356
- Expected weighted disagreement
- 0.340555
Calculation method
Calculation method
Quadratic weighted kappa
Quadratic distance weights ((i-j)/(k-1))²; kappa=1-Do/De.
Observed weighted disagreement
Quadratic distance weights ((i-j)/(k-1))²; kappa=1-Do/De.
Expected weighted disagreement
Quadratic distance weights ((i-j)/(k-1))²; kappa=1-Do/De.
Symbols and units
- a
- Ordered-category square contingency counts JSON
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Assumptions & limits
Assumptions & limits
- Ordered categories with quadratic disagreement weights; no confidence interval. Supported limits: All required fields must be finite values of the exact declared type and within displayed bounds. Lists contain1–1000 finite numbers. JSON tables are numeric rectangular rows, at most100 rows and30 columns; individual tools have stricter shape/domain constraints. Ordered categories with quadratic disagreement weights; no confidence interval.
Are these inputs private?
These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.