EDUCATION DECISION WORKBENCH
Fit voltage-current laboratory data to an ohmic model while exposing resistance, intercept, R squared, residuals, and power.
Review output
Load the worked example to inspect realistic inputs, or enter anonymous, policy-approved information. Results remain in this browser tab until you copy or download them.
Transparent method
The least-squares equation is V = RI + b. Fitted resistance R equals Σ[(I − mean I)(V − mean V)] divided by Σ(I − mean I)². Intercept b equals mean V − R mean I. Each point resistance is V/I, electrical power is VI, and each residual is measured voltage minus fitted voltage. R squared summarizes fit only within the supplied data range. A high R squared and small intercept support, but do not prove, an ohmic model for the tested range. A changing V/I pattern or curved residuals can indicate temperature change or a non-ohmic device. Compare fitted resistance with component tolerance and measurement uncertainty, and state which quantity was placed on each graph axis before interpreting slope.
Confirm the question, variables, units, source data, measurement precision, and the model expected by the current course or laboratory. Keep an untouched copy of observations before excluding or transforming any value.
Run the worked example first, inspect every intermediate row, compare units and signs, and look for a result that conflicts with the graph, physical system, chemical equation, or learning objective.
Record the method, assumptions, units, rounding, data limitations, and any excluded observations. Compare the exported report with an independent calculation or approved reference before assessment or laboratory use.
How to interpret the result
Use this analyzer for an Ohm’s law investigation or component characterization when learners must distinguish the slope of a V against I graph from individual V/I values. The residuals and intercept support discussion of instrument offset, contact resistance, heating, and non-ohmic response. It is an analysis tool for teacher-approved low-risk data, not instructions for building or energizing a circuit.
The resistor example includes five voltage-current pairs near a 20 Ω relationship. The workbench fits voltage as the dependent variable and current as the independent variable, reports the slope as fitted resistance, keeps the intercept visible, calculates V/I and VI for each point, and displays voltage residuals so departures from the line are reviewable.
The least-squares equation is V = RI + b. Fitted resistance R equals Σ[(I − mean I)(V − mean V)] divided by Σ(I − mean I)². Intercept b equals mean V − R mean I. Each point resistance is V/I, electrical power is VI, and each residual is measured voltage minus fitted voltage. R squared summarizes fit only within the supplied data range.
A high R squared and small intercept support, but do not prove, an ohmic model for the tested range. A changing V/I pattern or curved residuals can indicate temperature change or a non-ohmic device. Compare fitted resistance with component tolerance and measurement uncertainty, and state which quantity was placed on each graph axis before interpreting slope.
Do not fit mixed components, divide by zero current, use milliampere values as amperes, force the intercept to zero without method approval, call R squared measurement accuracy, or ignore power heating. Verify meter ranges, leads, zero offsets, component ratings, uncertainty, and laboratory safety through the responsible teacher before collecting or interpreting data.
Learning boundary
This browser-local workbench calculates only from the entered model and observations. It cannot validate an experiment, balance an equation, establish causation, certify laboratory safety, diagnose learning, or replace the method required by a teacher, curriculum, laboratory, or institution.
Review checklist
Practical questions
No. The calculation runs in the current browser tab. Avoid personal information and retain laboratory data in the system authorized by the school.
Treat it as an editable analysis aid. Show your own reasoning, cite the required sources, follow academic-integrity rules, and use the method requested by your course.
No. Check dimensions, graph shape, assumptions, measurement uncertainty, significant figures, and whether another model explains the observations better.
YOUR NIRMION
