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- 14:57, 6 September 2019 Graduate Level: Intro to Probability and Statistics (hist) [6,273 bytes] Foojt (talk | contribs) (Created page with "__NOTOC__ = Lecture Notes = <div class="mainpage_row"> <div class="mainpage_box"> <table cellspacing="0"> <tr> <td rowspan="2"><font size="40" face="arial">01</font></t...")
- 13:11, 6 September 2019 Full Lecture 18 (hist) [160 bytes] Foojt (talk | contribs) (Created page with "{{#lst:Lecture 18. A) Multicollinearity|section1}} {{#lst:Lecture 18. B) Partitioned Regression|section1}} {{#lst:Lecture 18. C) Gauss-Markov Theorem|section1}}")
- 13:09, 6 September 2019 Full Lecture 17 (hist) [217 bytes] Foojt (talk | contribs) (Created page with "= Lecture 17 = {{#lst:Lecture 17. A) Ordinary Least Squares|section1}} {{#lst:Lecture 17. B) Normal Linear Model|section1}} {{#lst:Lecture 17. C) Asymptotic Properties of OLS...")
- 18:52, 5 September 2019 Lecture 18. C) Gauss-Markov Theorem (hist) [4,180 bytes] Foojt (talk | contribs) (Created page with "= Gauss-Markov Theorem = The Gauss Markov theorem is an important result for the OLS estimator. It does not depend on asymptotics or normality assumptions. It states that, in...")
- 18:50, 5 September 2019 Lecture 18. B) Partitioned Regression (hist) [6,685 bytes] Foojt (talk | contribs) (Created page with "= Partitioned Regression = Partitioned regression is a method to understand how some parameters in OLS depend on others. Consider the decomposition of the linear regression e...")
- 18:48, 5 September 2019 Lecture 18. A) Multicollinearity (hist) [2,721 bytes] Foojt (talk | contribs) (Created page with "= Multicollinearity = Consider the case where <math display="inline">X</math> is given by: <math display="block">X=\overset{\begin{array}{ccc} \beta_{0} & \beta_{1} & \beta_...")
- 18:47, 5 September 2019 Lecture 17. D) Bootstrapping (hist) [2,924 bytes] Foojt (talk | contribs) (Created page with "= Bootstrapping = The origin of the term “bootstrapping” may relate to someone pulling themselves up by their own boot straps/laces. In a sense, it means making do with l...")
- 18:46, 5 September 2019 Lecture 17. C) Asymptotic Properties of OLS (hist) [8,555 bytes] Foojt (talk | contribs) (Created page with "= Asymptotic Properties of OLS = We now allow, * <math display="inline">X</math> to be random variables * <math display="inline">\varepsilon</math> to not necessarily be no...")
- 18:42, 5 September 2019 Lecture 17. B) Normal Linear Model (hist) [10,278 bytes] Foojt (talk | contribs) (Created page with "= Normal Linear Model = In the previous example, the notion of random variable wasn’t mentioned. We simply wanted to draw a predictive line along some points. In this examp...")
- 18:37, 5 September 2019 Lecture 17. A) Ordinary Least Squares (hist) [3,399 bytes] Foojt (talk | contribs) (Created page with "= Ordinary Least Squares = Suppose we have some data <math display="inline">\left\{ x_{i},y_{i}\right\} _{i=1}^{N}</math>. We would like to relate it through a line, i.e., A...")