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Returns a Reducer that computes the slope and offset for a (weighted) linear regression of 2 inputs. The inputs are expected to be x data followed by y data. Where applicable, the outputs are named: "scale", "offset".
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-19 UTC."],[[["\u003cp\u003eThis reducer calculates the slope (scale) and y-intercept (offset) of a linear regression line.\u003c/p\u003e\n"],["\u003cp\u003eIt uses two input datasets: one for the x-values and one for the corresponding y-values.\u003c/p\u003e\n"],["\u003cp\u003eWeights can be applied to the data points for a weighted linear regression.\u003c/p\u003e\n"],["\u003cp\u003eThe output of the reducer contains the calculated slope and offset, labeled as "scale" and "offset" respectively.\u003c/p\u003e\n"]]],["This document describes a Reducer, `ee.Reducer.linearFit()`, which calculates a linear regression between two inputs, x and y data. It determines the slope (\"scale\") and y-intercept (\"offset\") of the linear relationship. This Reducer operates without any input arguments and returns a Reducer object. The core function is computing these two parameters, scale and offset, representing the best-fit line for the provided data.\n"],null,["# ee.Reducer.linearFit\n\nReturns a Reducer that computes the slope and offset for a (weighted) linear regression of 2 inputs. The inputs are expected to be x data followed by y data. Where applicable, the outputs are named: \"scale\", \"offset\".\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------|---------|\n| `ee.Reducer.linearFit()` | Reducer |\n\n**No arguments.**"]]
