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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 10 we'll do Lab 8 which will become HW 9.
mini videos on quantile regression, propensity score, nonparametric and LASSO and spike-slab. Lecture notes all the rest.
videos on causation and IV, multilevel models and tree, forest, and SVM. Lecture notes 120 - 145.
Lab 7 .
Homework 7
Homework 6
In class Oct 20 27 we'll do Lab 6 which will become HW 6 HW 7.
Wave 48 of HHPulse data.
video on data sets. Here is text that we will discuss in class.
videos on more MOAR dummy variables and logit/probit models. Lecture notes 118-120 and 139-145.
videos on Nonlinear regression and More on dummy variables. Lecture Notes pages 106 - 118.
In class Oct 6 we'll do Lab 5
videos on Dummies and Factors and Hypothesis Tests and Heteroskedasticity. Lecture Notes pages 98 - 105.
Homework 4 (with lab4 on GitHub repo)
In class Sept 22 we'll do Lab 4 although we will be on zoom for that week's class
videos on OLS regression and multivariate regression. Please review Lecture Notes pages 81 - 97.
Homework 4 (with lab3 on GitHub repo)
In class Sept 15 we'll do Lab 3 which will become HW4 FML
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
Next dataset, ACS NY (which I sometimes refer to as PUMS)
In class Sept 8 we'll do Lab 2
Homework 2
Please review these videos before class,
on continuous random variables and "Is That Big?"
Please review these videos before class,
on probability (part a), part b and discrete random variables. Note on extreme values.
Homework 1 due Sept 2 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Sept 1 we'll do Lab 1
More of R Basics for Lecture 1
some details about using RStudio
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
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.
↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ these are what to do before class
R Basics for Lecture 1 requires that you download PUMS data above
Video 1 on basics for R - these go along with the R Basics for Lecture 1 page above and video on simple background about markdown,
Preliminaries: refresh your basic stats knowledge for Diagnostic Test early, start to learn R
↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ stuff below is background
Syllabus includes more references for learning R
specific skills to be learned in this course
Grading Policy
My expectations of you and your expectations for me
A Refresher on Basic Skills. Most of it should be too basic but it
needs to be communicated.
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