Stats

CPaT Modeling and Statistics, Spring 2013

Information about this part of the program can be found in the Syllabus.  However, faculty might alter the schedule during the quarter, so check each week’s assignment below (current week will always be first)!

On our fileshare (Handouts\Stats)
Tips and Tricks for Excel – from Robyn (our tutor)
Error Bars in Excel (for Week 3 Lab) – from Judy

Keys posted:

  1. Midterm DRAFT — possibly to be updated Thursday
  2. Lab Keys
    Stats Lab – Week 7 – coming …
    All Other Stats Labs
    In addition, for Week 5 lab (ANOVA) I posted a .doc and well as a new .xlsx

Week 10 –  Final Exam and Portfolio’s due!

  • Stats Problems for the Final Take Home Exam will be posted by Friday, Week 8, so you can start working on that.  You’ll have the rest (1) a Stella model problem (2) two ‘essay’ questions by Monday Week 9.
  • TUESDAY Week 10, 9:30am.  Exam and Portfolios due   No Exceptions
  • TUESDAY Week 10, 9:30 – 10:15.  In Class Moodle Quiz –  in Lab.

Week 9 – R – an Open Source Alternative to JMP

  • No reading for this week – use the time to catch up and start reviewing for the final!
  • Monday Lecture :  no class – Memorial day
  • Tuesday Lab: R
  • Wednesday Lecture:
    Walkthrough Bob’s slides and animations
    Data archives
    Recap ANOVA – labs to return (today & tomorrow)
    R – what’s due if you do Part I?
    Exam – questions?
    other questions?
  • help sessions
    with Robyn – Monday 3-5 Lib 2617
    with Kara – Friday 2-4! (place TBA – Lib 2617?)

Week 8 – Scientific Visualization in Processing

  • No reading for this week – use the time to catch up and start reviewing for the final!
  • Monday Lecture :  Judy demo’d CART model for the Iris data set.  Lee Zeman introduced us to Processing – See ProcessingOverview
  • Tuesday Lab will be in the fileshare Handouts and at:  http://alala.evergreen.edu/misc/processing/Processinglab.html
  • Wednesday Lecture:  Review and midterm debrief
  • Thursday, in ML Lab – Midterms and Labs returned.

Wednesday Lecture :

Debrief Processing Lab –
–  review code — if you still have questions see Judy’s example code (as text)

Quick Overview of Stats Learned….  (see slides –  and  “the Flowchart“).  Outline of the Quick Overview (what is in the slides) is below.

  1. Asking questions in Science
  2. Experimental Design – replication and randomization, independence (confounding factors) – Manipulation and Natural Experiments
  3. The Matrix (and the statistical tests we studied)
  4. Interpreting and reporting the results

Week 7 -Chi-Square

  • Reading: Focus on one-way contingency tables – Ch. 10 pp. 349-358.
  • Monday Lecture.  Judy’s lecture notes on Chi-Square, but most of what we did was a chalk talk – of the example in the Tuesday Lab (see below).
  • Tuesday Lab – on Handouts\Stats week 6.   Weekly Quiz, moodle in class – you must be in the lab.  No notes, no books.  Lab on Regression.
  • Wednesday Lecture.  Debrief of h2olos Stella Model (Midterm) – key will be posted on Tuesday – not all exams are in yet; debrief lab – question was about how to organize 1kcs data for chi-square analysis and make a contingency table.  Answer is:  load the 1kcs data into JMP, do the chi-square, then use JMP output to make your contingency table!

Week 6 – Regression

  • Reading:
    By Tuesday, Ch. 9 on Regression pp. 239-266.
    By Wednesday, Ch. 8 – Managing and Curating Data, review pp 223-236 (Data Transformations)
  • Monday Lecture.  Regression — chalk talk – lecture notes are minimal – see book to review.  Curious about midterm?  See MidTermFAQs
  • Tuesday Lab – on Handouts\Stats week 6.   Weekly Quiz, moodle in class – you must be in the lab.  No notes, no books.  Lab on Regression.
  • Wednesday Lecture.  More about regressions; debrief lab.  See GalapagosDemoScript and GalapagosData.
    if time – talk about what to expect on the final exam….

Monday Lecture notes:

  • Regression….  when you have a continuous X and a continuous Y
    -What regression (fitting a line to a set of points) does for you!
    -How do you know you’ve got a ‘good’ line?  (parametric, resampling)
    -What to do if you don’t?
  • Some more advanced topics (Wednesday)
    -data transformation
    -multiple regression (more than 1 X)
    -logistic regression (categorical Xs)

Week 5:

  • Reading:
    By Tuesday, Ch. 10 on 1-way ANOVA pp. 289-300.
    By Wednesday, Ch. 8 – Managing and Curating Data
  • Monday Lecture.  ANOVA recap – Excel Sheet with Simple Example – chalk talk – lecture notes are minimal – see book to review.
  • Tuesday Lab – on Handouts\Stats week 5.   Midterm Quiz, On the moodle, in class – you must be in the lab.  No notes, no books.  Lab will be short:  review 1-way ANOVA, Week 4 Lab due (review in lab).
  • Wednesday Lecture.  Review (ask Questions); Distribute Take home Midterm; Lecture on Managing & Curating Data

Week 4:  By Tuesday, Read Bestiary of Experimental & Sampling Designs; Intro to ANOVA, (Ch. 7).  See Lecture notes for reading priorities.
Monday Lecture.  Tuesday Lab – on Handouts\Stats week 4.  Wednesday Lecture (none).

Week 3:   By Tuesday, read Ch. 4,5.  Quiz in Lab at 9:30.  By Wednesday, read Ch. 6.
Monday Lecture.  Tuesday Lab – on Handouts\Stats week 3.  Wednesday Lecture.

Week 2:  Monday Lecture.  Tuesday Lab – Handouts\Stats week 2.  Wednesday Lecture.

Week 1: