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Some class resources are on Blackboard
In class Nov 19, we'll be doing Lab 8
Before class Nov 19, please: review these mini videos on quantile regression,
nonparametric regression,
propensity score,
LASSO and Spike-Slab,
and trees, forests and support vector machines
Looking forward to the second exam on Nov 26 here are some practice problems from past exams
In class Nov 12, we'll be doing Lab 7, leading into HW 7
Before class Nov 12, please: review these videos on instrumental variables and limited dependent models
In class Nov 5, we'll be doing Lab 6, leading into HW 6
Before class Nov 5, please:
details about final project
In class Oct 29, we'll be doing Lab 5, leading into HW 5 (and here is Exam 1 for ref)
Before class Oct 29, please:
Looking forward to the first exam on Oct 22 here are some practice problems from past exams
Before class Oct 15 (note that we will start late since there's Conversation in Leadership event before, http://bit.ly/2ke1cUz), please:
In class Sept 24, we'll be doing Lab 4, leading into HW 4
Before class Sept 24, please:
In class Sept 17, we'll be doing Lab 3, don't worry about HW 4 just do the Hawkes
Before class Sept 17, please:
In class Sept 10, we'll be doing Lab 2, leading into HW 3
Before class Sept 10, please:
In class Sept 3, we'll be doing Lab 1, leading into HW 2 (which, recall, you upload on Blackboard). Also here is the markdown file that creates Lab 1
Before class Sept 3, please:
- review lecture notes pp 32 - 46
- review these videos on probability and discrete random variables
- bring the dice from HW1
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 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.
Homework 1 due next week Sept 3 (well, by midnight somewhere on the globe so I'll accept uploads on Blackboard until 8am in NYC the next day)
Powerpoint about beginning stats (with voice narration)
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.
R Basics for Lecture 1 requires that you download PUMS data above
Video 1 on basics for R in ppt or
as video - these go along with the R Basics for Lecture 1 page above
the markdown file for R Basics, although delete the .txt in filename
Preliminaries: refresh your basic stats knowledge for Diagnostic Test early, start to learn R
Syllabus includes more references for learning R
specific skills to be learned in this course
Grading Policy
High-Risk
Grade Option due by Sept 25 if you want the risky option
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.
Tweets
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