Course resources

All lecture slides, readings, and labs will be posted here each week. Assignments will be mentioned here, but all assignments will be posted, submitted, and graded through Github Classrooms.

Week 0

• Slides: Course intro html pdf

• Reading:

  • Preface, Chapters 1-2, Introduction to Modern Statistics
  • (Optional) Gender Shades
  • (Optional) RCTs

• Assignment: Assignment 1 posted 9/28; due 10/09, 5pm (note: usually will be Fridays, this deadline allows you more time to get set up on GitHub Classroom)

Week 1

• Slides: Summary statistics html pdf

• Reading:

  • Chapters 4-6, Introduction to Modern Statistics
  • New Video on variance

• Discussion Section (Wednesday): EDS 222 Lab setup and Github Classrooms demo (taught by TA, Sandy Sum)

  • Lab markdown
  • Lab html
  • _common.R setup script

• Lab (Thursday): Summarizing categorical and numerical data in R

  • Lab markdown
  • Lab answers
  • Lab answers in html

• Assignment: Assignment 1 due 10/09, 5pm

Week 2

• Slides: Ordinary Least Squares html pdf

• Reading:

  • Chapter 7, Introduction to Modern Statistics
  • New Video on covariance
  • New Linear regression with one regressor Econometrics with R

• Discussion Section (Wednesday): Law of Large Numbers, simulation in R

  • HW 1 Answers

• Lab (Thursday): Correlations and ordinary least squares in R

  • Lab markdown
  • Lab answers
  • Lab answers in html

• Assignment: Assignment 1 due 10/09, 5pm. Assignment 2 posted 10/10 and due 10/20 at 5pm.

Week 3

• Slides 04: Ordinary Least Squares, continued html pdf

• Reading:

  • Chapter 8, Introduction to Modern Statistics
  • Optional/Extra: Ch 10, Introduction to Modern Statistics

• Discussion Section (Wednesday): Materials to be distributed in class

• Lab (Thursday): OLS in R, continued markdown, answers, answers in html.

• Final project guidelines: link

• Assignment: Assignment 2 due 10/20, 5pm

Week 4

• Slides 05: Multiple linear regression and interaction models html pdf

• Reading:

  • Chapter 8, Introduction to Modern Statistics
  • Note that interaction models are not covered in IMS. This is a helpful resource that covers interaction models conceptually and shows how to code them up in R (note that we will cover the coding in Thursday’s lab and please ignore anything after the subsection titled “Obtaining standard errors for marginal effects”)

• Lab: Interaction models in R markdown, answers, answers in html.

• Assignment: Assignment 3 will posted 10/24, due 11/07 and graded pass/fail.

  • Answer key for exam studying: markdown html

• Extra exam study material:

  • Answers to practice questions here
  • Here is an example of how you can evaluate the plausibility of OLS Assumption #4, which is necessary to ensure that OLS is the lowest variance estimator of all the unbiased estimators: html, markdown
  • Here is a derivation of omitted variables bias pdf

Week 5

• Tuesday: Stats in (climate) action link

• Thursday: Midterm Exam

• Assignment 2 answer key: html rmd

Week 6

• Slides 06: Logistic regression and other nonlinear models html pdf

• Reading:

  • Chapter 9, Introduction to Modern Statistics

• Lab: Logistic regressions in R markdown, answers, answers in html

• Assignment: Final project proposal due November 10, 5pm (email to Sandy at sandysum@ucsb.edu). Final project guidelines here

Week 7

• Slides 07: Inference and hypothesis testing html pdf

• Reading:

  • Chapters 13, 19.2

• Lab: Inference and hypothesis testing in R markdown, answers, answers in html

• Assignment: Assignment 4 (posted TBD, due TBD)

Week 8

• Slides 08: Time series analysis html pdf

• Reading: Chapters 2 and 3 in Hyndman & Athanasopoulos

• Chapters 2, 3, Forecasting: Principles and Practice

• Lab: None! Happy Thanksgiving

• Assignment: Assignment 4: Hypothesis testing and time series analysis (due 12/08, 5pm)

Week 9

• Slides 09: Spatial interpolation and kriging html pdf

• Lab: Kriging in R [likely postponed to Week 10]

• Assignment: Assignment 4: Hypothesis testing and time series analysis (due 12/08, 5pm)

Week 10

• Lab (Tuesday 12/05): Kriging in R data markdown, answers, answers in html.

• Thursday 12/07: Guest lecture by Anna Boser, Bren PhD student. Stats in ecohydrology!

• Assignment: Assignment 4: Hypothesis testing and time series analysis (due 12/08, 5pm)

• Final projects next week! Presentations: 12/12 4-7pm, see schedule and upload materials here