{"batchcomplete":"","continue":{"lecontinue":"20190905234930|1036","continue":"-||"},"query":{"logevents":[{"logid":1046,"ns":6,"title":"File:Jensen1.png","pageid":667,"logpage":667,"params":{},"type":"create","action":"create","user":"Foojt","timestamp":"2019-10-10T21:55:44Z","comment":"Jensen's Inequality"},{"logid":1045,"ns":6,"title":"File:Jensen1.png","pageid":667,"logpage":667,"params":{"img_sha1":"sb2a76hdnw261yae2dfscx1r5698i1a","img_timestamp":"2019-10-10T21:55:44Z"},"type":"upload","action":"upload","user":"Foojt","timestamp":"2019-10-10T21:55:44Z","comment":"Jensen's Inequality"},{"logid":1044,"ns":6,"title":"File:Jensen.png","pageid":568,"logpage":568,"params":{"img_sha1":"sb2a76hdnw261yae2dfscx1r5698i1a","img_timestamp":"2019-10-10T21:54:45Z"},"type":"upload","action":"overwrite","user":"Foojt","timestamp":"2019-10-10T21:54:45Z","comment":""},{"logid":1043,"ns":0,"title":"Main Page","pageid":0,"logpage":1,"params":{},"type":"delete","action":"delete","user":"Foojt","timestamp":"2019-09-06T20:01:04Z","comment":"content was: \"==Grad-level Probability and Statistics Lecture Notes==   [[Lecture 1._A)_Sample_Space|Lecture 1: Probability Theory, Probability Space, Random Variables, Cumulative Distribution Function]]\""},{"logid":1042,"ns":0,"title":"Grad-level Probability and Statistics Lecture Notes","pageid":0,"logpage":529,"params":{},"type":"delete","action":"delete","user":"Foojt","timestamp":"2019-09-06T20:00:54Z","comment":"content was: \"__NOTOC__  <div class=\"mainpage_row\"> <div class=\"mainpage_box\"> <table cellspacing=\"0\">   <tr>     <td rowspan=\"2\"><font size=\"40\" face=\"ar...\", and the only contributor was \"[[Special:Contributions/Foojt|Foojt]]\" ([[User talk:Foojt|talk]])"},{"logid":1041,"ns":0,"title":"Graduate Level: Intro to Probability and Statistics","pageid":666,"logpage":666,"params":{},"type":"create","action":"create","user":"Foojt","timestamp":"2019-09-06T19:57:12Z","comment":"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...\""},{"logid":1040,"ns":0,"title":"Full Lecture 18","pageid":665,"logpage":665,"params":{},"type":"create","action":"create","user":"Foojt","timestamp":"2019-09-06T18:11:58Z","comment":"Created page with \"{{#lst:Lecture 18. A) Multicollinearity|section1}} {{#lst:Lecture 18. B) Partitioned Regression|section1}} {{#lst:Lecture 18. C) Gauss-Markov Theorem|section1}}\""},{"logid":1039,"ns":0,"title":"Full Lecture 17","pageid":664,"logpage":664,"params":{},"type":"create","action":"create","user":"Foojt","timestamp":"2019-09-06T18:09:24Z","comment":"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...\""},{"logid":1038,"ns":0,"title":"Lecture 18. C) Gauss-Markov Theorem","pageid":663,"logpage":663,"params":{},"type":"create","action":"create","user":"Foojt","timestamp":"2019-09-05T23:52:11Z","comment":"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...\""},{"logid":1037,"ns":0,"title":"Lecture 18. B) Partitioned Regression","pageid":662,"logpage":662,"params":{},"type":"create","action":"create","user":"Foojt","timestamp":"2019-09-05T23:50:50Z","comment":"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...\""}]}}