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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and vice versa. On the contrary, a negative correlation value indicates that as one variable increases, the other variable decreases, and vice versa. Squaring the correlation coefficient results in the coefficient of determination, denoted by 'r2' or 'R2'. This value ranges from 0 to 1. A value closer to 1, such as 0.999, indicates an excellent fit, whereas a value close to 0 indicates a poor fit.

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Calibration CurveCorrelation CoefficientLinear CorrelationCorrelation StrengthPositive CorrelationNegative CorrelationCoefficient Of DeterminationRPerfect CorrelationVariable Association

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1.18 : Calibration Curves: Correlation Coefficient

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1.1 : SI Units: 2019 Redefinition

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1.2 : Degrees of Freedom

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1.3 : Statistical Analysis: Overview

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1.4 : Types of Errors: Detection and Minimization

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1.5 : Systematic Error: Methodological and Sampling Errors

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1.6 : Random Error

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1.7 : Standard Deviation of Calculated Results

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1.8 : Introduction to z Scores

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1.9 : Uncertainty: Overview

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1.10 : Propagation of Uncertainty from Random Error

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1.11 : Propagation of Uncertainty from Systematic Error

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1.12 : Uncertainty: Confidence Intervals

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1.13 : Significance Testing: Overview

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1.14 : Identifying Statistically Significant Differences: The F-Test

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