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Some class resources are on Slack
Exam 2 to be completed between 4:50 and 7pm on Nov 23 during class
Homework 7 due Nov 16 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
Before class Nov 16, please:
Practice for Exam 2
In class Nov 9, we will do Lab 7
Before class Nov 9, please:
Homework 6 due Nov 9 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class we'll talk about final project. Review this video on other data sets that I've created
In class Nov 2, we will do Lab 6
Before class Nov 2, please:
Homework 5 due Nov 2 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Oct 26, we will do Lab 5
Before class Oct 26, please:
Here are Practice problems for exam
Before class Oct 14, please:
Homework 4 due Oct 14 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Oct 5, we will do Lab 4
Before class Oct 5, please:
Homework 3 due Oct 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 29, we will do Lab 3
Before class Sept 29 (note NOT Monday but Tuesday), please:
In class Sept 21, we will do Lab 2
Before class Sept 21, please:
Homework 2 due Sept 29 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
In class Sept 14, we'll do Lab 1
Before class Sept 14, please:
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 Sept 14 (well, by midnight somewhere on the globe so I'll accept uploads on Slack until 8am in NYC the next day)
For first class, use zoom link (on Slack channel). Download zoom.
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 - these go along with the R Basics for Lecture 1 page above
the markdown file for R Basics,
simple background about markdown (video),
about how the class will go in Fall 2020 with distance learning (video),
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
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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