Description
Accept model log-level or level-level.
Regression model terms depend on the response link, variable scale and the meaning of coefficients. A local marginal effect, elasticity or model mean computed from supplied parameters does not estimate those parameters or establish causality.
Accept model log-level or level-level.
When to use this tool
Use Elasticity Log Level One Point Term when checking one stated regression-model contribution.
How to use the tool and how it works
- Enter coefficient, regressor, model, fittedOutcome. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read elasticity. Use the output convention below.
Accept model log-level or level-level. For log-level return elasticity = coefficient*regressor; for level-level require fittedOutcome !== 0 and return coefficient*regressor/fittedOutcome.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| coefficient | Coefficient | Required. Finite number. |
| regressor | Regressor | Required. Finite number. |
| model | log-level means natural-log outcome versus level regressor; level-level uses both in levels. | Required. Text; one of "log-level", "level-level"; maximum UTF-16 length 8192. |
| fittedOutcome | Fitted Outcome | Required. Finite number. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| elasticity | Elasticity | Required. Finite number. |
{
"input": {
"coefficient": 0.2,
"regressor": 5,
"model": "log-level",
"fittedOutcome": 10
},
"output": {
"elasticity": 1
}
}Limitations and assumptions
- The model label uses natural-log outcome for log-level. This is one local elasticity under supplied coefficients, not a log-log coefficient, fitted model or causal effect.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
- Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.
Alternative or Complementary methods
Use a fitted model, suitable data and defensible identification for prediction, inference and causal interpretation.
References
Similar or alternative tools
- Count Model Exposure Log Offset Term
logOffset = ln(exposure); expectedCount = exposure*exp(linearPredictor).
- Logit Marginal Effect One Covariate
effect = coefficient*probability*(1-probability).