joby_m_anthony_iii.bib

@book{burdenNumericalAnalysis2019,
  title = {Numerical {{Analysis}}},
  author = {Burden, L., Richard and Faires, J., Douglas and Burden, M., Annette},
  year = {2019},
  edition = {Tenth},
  publisher = {{Cengage}},
  address = {{20 Channel Center Street, Boston, MA 02210, USA}},
  isbn = {978-1-305-25366-7},
  langid = {english}
}

@misc{thebrightsideofmathematicsQRDecompositionSquare2020,
  title = {{{QR}} Decomposition (for Square Matrices)},
  author = {{The Bright Side of Mathematics}},
  year = {2020},
  month = mar,
  abstract = {Support the channel on Steady: https://steadyhq.com/en/brightsideofm... Official supporters in this month: - William Ripley - Petar Djurkovic - Mayra Sharif - Dov Bulka  - Lukas M\"uhrke - Khan El - Marco Molinari  - Andrey Kamchatnikov  - Benjamin Bellick  - Sarah Kim  This video is about the QR decomposition for square matrices. I show the algorithm with the help of an example.  I hope that this helps students, pupils and others. \#LinearAlgebra (This explanation fits to lectures for students in their first year of study: Mathematics for physicists, Mathematics for the natural science, Mathematics for engineers and so on)}
}

@misc{weissteinLeastSquaresFitting,
  type = {Text},
  title = {Least {{Squares Fitting--Power Law}}},
  author = {Weisstein, Eric W.},
  publisher = {{Wolfram Research, Inc.}},
  abstract = {Given a function of the form  y=Ax\^B,  (1)   least squares fitting gives the coefficients as b = (nsum\_(i=1)\^(n)(lnx\_ilny\_i)-sum\_(i=1)\^(n)(lnx\_i)sum\_(i=1)\^(n)(lny\_i))/(nsum\_(i=1)\^(n)(lnx\_i)\^2-(sum\_(i=1)\^(n)lnx\_i)\^2) (2)  a = (sum\_(i=1)\^(n)(lny\_i)-bsum\_(i=1)\^(n)(lnx\_i))/n, (3)   where B=b and A=e\^a.},
  copyright = {Copyright 1999-2022 Wolfram Research, Inc.  See https://mathworld.wolfram.com/about/terms.html for a full terms of use statement.},
  howpublished = {https://mathworld.wolfram.com/},
  langid = {english},
  file = {C\:\\Users\\jmanthony1\\Zotero\\storage\\M3JCXZVD\\LeastSquaresFittingPowerLaw.html}
}