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Some class resources are on Slack
Using class material: Each week I'll be putting up links to video tutorials. You should review those BEFORE class. We'll use class time for labs, questions and to put that new knowledge to use.
How do lecture notes relate to videos? Notes have more detail including both more basic and more advanced material. Why? Because skimming text is much easier than skimming video so I've included more in the notes. You should take time to review the sections of the lecture notes that correspond with each video.
As you work in data, you'll hear the phrase, "the map is not the territory" along with citations to the story by Jorge Luis Borges, "On Rigor in Science". This course is not set up in an easy linear way because that's not how knowledge works -- we may lure in newbies with that fiction but this is grad school. You have a choice about how deep you want to go.
In class Nov 14 we'll do Lab 9
Please review these videos before class Nov 14,
on instruments ,
on multilevel models,
mini on trees and forests
mini on quantile regression ,
mini on propensity scores,
mini on nonparametric regression,
mini on LASSO and spike and slab,
mini on neural net,
along with all the lecture notes
In class Nov 7 we'll do Lab 8 ,
then after class (before Lab 9) please work through
Lab 8 addendum
Please review these videos before class Nov 7,
even more dummies! ,
limited dependent variables,
along with lecture notes up to p.118
In class Oct 31 we'll do Lab 7
Please review these videos before Oct 31 class,
on nonlinear regression and more on dummies , along with lecture notes up to p.118
Homework 7 due Oct 31 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Oct 24 we'll do Lab 6 ;
we'll also discuss final project
so you can learn more about additional data sets ,
Oct 17 is first exam
In class Oct 10 we'll do Lab 5
Please review these videos before class,
on dummies and hypothesis tests, along with lecture notes
Homework 5 due Oct 10 since no class Oct 3 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Sept 26 we'll do Lab 4
Please review these videos before class,
on linear models part 1 and part 2, along with lecture notes and textbook up to Ch 6
Homework 4 due Sept 26 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Sept 19 we'll do Lab 3
Please review these videos before class,
on hypothesis testing part 1 and part 2 and on p-values, effect size and sample size and finally on k-nn classifiers These are in Lecture Notes pp 63-80
Homework 3 due Sept 19 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Sept 12 we'll do Lab 2
Please review these videos before class,
L 3.1 continuous rvs, L 3.2 Is That Big? and L 3.3 on extreme values .
Homework 2 due Sept 12 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
Homework 1 due Sept 5 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Sept 5 we'll do Lab 1
Please review these videos before class,
L 2.1 on probability (part a), L 2.2 part b and L 2.3 discrete random variables.
Class Lecture Notes up to p 50 should be review and of course this is a draft, might change as we go along particularly parts after p 150 or so
Video 1.2 More R Basics with notes to go along
Video 1.1 on basics for R and Notes to go with Video 1.1 make sure to download Household Pulse data
Preliminaries: refresh your basic stats knowledge for Diagnostic Test early, start to learn R.
Download and install both R and R Studio.
Syllabus includes more references for learning R
Grading Policy
A Refresher on Basic Skills. Most of it should be too basic but it
needs to be communicated.
I've got several datasets in R format for you
The zip file includes the data as well as some details about how I created it. To begin you will likely only need the RData file.
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