sensortoolkit.calculate._regression_stats
This module computes statistical metrics for the Ordinary Least Squares linear regression between sensor and FRM/FEM datasets (FRM/FEM as the dependent variable along the x-axis and sensor data as the independent variable along the y-axis).
Bias and linearity
U.S. EPA’s Performance Targets Reports recommend using a linear regression model, relating sensor and FRM/FEM measurements, to determine the magnitude of bias and linearity. The regression model takes the form
where
\(y\) = sensor measurements
\(x\) = FRM/FEM measurements
\(m\) = regression slope
\(y\) = regression intercept
The slope \(m\) and intercept \(y\) indicate the degree of systematic bias between sensor and reference measurements.
Linearity is measured via the coefficient of determination (\(R^2\)).
- @Author:
- Samuel Frederick, NSSC Contractor (ORAU)U.S. EPA / ORD / CEMM / AMCD / SFSB
- Created:
Tue Mar 3 13:47:32 2020
- Last Updated:
Tue Jul 13 11:09:13 2021
Functions
Save a pandas DataFrame to a comma-separated value file. |
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Combine 1-hour and 24-hour regression statistics DataFrames. |
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Compute OLS regression statistics. |