Supriya Malla
Cornell University, Ithaca, NY 14850, USA

Ganesh Malla
University of Cincinnati-Clermont College, Batavia, OH, 45103, USA

### DOI

#### Keywords

Affine, function, derivative, inverse informative matrix, least-squares estimate, regression coefficient

#### Abstract

Background: Arguably the most frequently used term in science, particularly in mathematics and statistics, is linear. However, confusion arises from the various meanings of linearity instructed in different levels of mathematical courses. The definition of linearity taught in high school is less correct than the one learned in a linear algebra class. The correlation coefficient of two quantitative variables is a numerical measure of the affinity, not only linearity, of two variables. However, every statistics book loosely says it is a measure of linear relationship. This clearly show that there is some confusion between use of the terms the linear function and affine function.

Objectives: This article aims at clarifying the confusion between use of the terms linear function and affine function. It also provides more generalized forms of the gradient in different branches of mathematics and show their equivalency.

Material and Methods: We have used the pure analytical deductive methods to proof the statements.

Results: We have clearly presented that gradient is the measure of affinity, not just linearity. It becomes a special case of the derivative in calculus, of the least-squares estimate of the regression coefficient in statistics and matrix theory. The gradient can be seen in terms of the inverse of the informative matrix in the most general setting of the linear model estimation.

Conclusion: The article has been clearly written to show the distinction between the linear and affine functions in a concise and unambiguous manner. We hope that readers will clearly see various generalizations of the gradient and article itself would be a simple exposition, enlightening, and fun to read.

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