The coefficient of assurance is a factual estimation that analyzes how contrasts in one variable can be clarified by the contrast in a second variable when anticipating the result of a given occasion. In simple words, this coefficient, which is also known as R-squared examines the direct relationship between variables.

The relationship between variables is also known as the **"goodness of fit,"** is denoted as a value between 0.0 and 1.0. A value of 1.0 shows a perfect fit, and is hence an exceedingly solid show for future figures, whereas a value of 0.0 demonstrates that the calculations did not measure the data accurately. But a value of 0.20, for example, suggests that 20% of the dependent variable is predicted by the independent variable, whereas a value of 0.60 suggests that 60% of the dependent variable is predicted by the free variable, and so forth.

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