Editing Jacobian matrix and determinant (section)

===Regression and least squares fitting===
The Jacobian serves as a linearized [[design matrix]] in statistical [[regression analysis|regression]] and [[curve fitting]]; see [[non-linear least squares]]. The Jacobian is also used in random matrices, moments, local sensitivity and statistical diagnostics.<ref>{{cite journal|last1=Liu|first1=Shuangzhe |last2=Leiva|first2=Victor|last3=Zhuang|first3=Dan|last4=Ma|first4=Tiefeng | last5=Figueroa-Zúñiga | first5=Jorge I.|date=March 2022|title=Matrix differential calculus with applications in the multivariate linear model and its diagnostics|journal=Journal of Multivariate Analysis |volume=188|pages=104849|doi=10.1016/j.jmva.2021.104849|doi-access=free}}</ref><ref>{{Cite journal| last1=Liu|first1=Shuangzhe|
last2= Trenkler|first2=Götz| last3=Kollo|first3=Tõnu| 
last4=von Rosen|first4=Dietrich| 
last5=Baksalary|first5=Oskar Maria| 
date= 2023|
title= Professor Heinz Neudecker and matrix differential calculus| 
journal= Statistical Papers |volume=65 |issue=4 |pages=2605–2639 | language=en | doi= 10.1007/s00362-023-01499-w|s2cid=263661094 }}</ref>