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Last updated: Thu Sep 26 2024, 14:47

Syllabus

Welcome to the course website for STA 209, Introduction to Applied Statistics. To begin, you can find the course syllabus linked below:

You can locate course content by scrolling, or by using the navigation bar in the upper left.

Please note: material will not be posted until we’ve reached that point in the course.

Course Materials

Exam #1 Content

Week 1 (Intro)

Date Lecture Lab Reading
Mon 1/22 Syllabus - -
Wed 1/24 Introduction In-class -
Fri 1/26 Intro to R Intro R Lab 1.2, 1.3 from IMS

Assignments and Deadlines: None

Week 2 (Data Visualization)

Date Lecture Lab Reading
Mon 1/29 Data Visualization - 4.2.1, 5.1 from IMS
Wed 1/31 Data Visualization cont. ggplot2 4.1, 4.3, 4.6
Fri 2/2 ggplot and aesthetics Continue Lab -

Assignments and Deadlines:

  • Lab 01 due Monday, Feb 05 at 11:59pm

Homework 1 due Monday, Feb 05 at 11:59pm

Note: Homework should be neatly formatted with questions specified as headers and with your R code and written solutions underneath. Here is an example of how it should look.

Week 3 (Numerical Summaries)

Date Lecture Lab Reading
Mon 2/5 Tables Wrap up lab -
Wed 2/7 Cont. tables Table Lab -
Fri 2/9 Numerical Summaries Table Lab cont. -

Assignments and Deadlines:

  • Lab 02 due Monday, Feb 12 at 11:59pm
  • Lab 03 due Monday, Feb 12 at 11:59pm
  • Homework 2 due Mon, Feb 12 at 11:59pm

Extra Practice:

Our ggplot2 lab really only covered the basics. You can get in-depth practice if you want to trick out your graphs by going through STA-230 labs which do not require any additional knowledge than what we have already acquired:

NOTE Exam 1 will be February 21 – details to follow

Week 4 (Regression)

Date Lecture Lab Reading
Mon 2/12 Correlation - -
Wed 2/14 Linear Regression Regression -
Fri 2/16 Linear Regression (Categorical Predictor) - -

Assignments and Deadlines:

Practice exam posted here. Note that you can skip/ignore questions about risk and relative risk, as well as those with multivariate regression

Here is a rough sketch of the topics that may be covered. I will continue to fill in with more examples as the week progresses

Week 5 (Exam Week)

EXAM REMINDERS:

  • In Noyce 2022
  • Bring calculator
Date Lecture Lab Reading
Mon 2/19 Review Day - -
Wed 2/21 Exam 😎 - -
Fri 2/23 dplyr package dplyr -

Assignments and Deadlines:

None

Exam #2 Content

Assignments now due on Fridays

Week 6 (Confidence Intervals)

Date Lecture Lab Reading
Mon 2/26 Sampling - -
Wed 2/28 Intervals Fish -
Fri 3/1 Bootstrapping Bootstrapping IMS Ch 12

Assignments and Deadlines:

  • Course Suvey is now available to provide feedback
  • dplyr lab due Friday, March 01 at 11:59 pm
  • Homework 4 due Friday, March 08 at 11:59 pm
  • Bootstrap lab due Friday, March 08 at 11:59 pm

Week 7 (Normal Approximation)

Date Lecture Lab Reading
Mon 3/4 Normal Approximations - -
Wed 3/6 Proportions and \(t\) More Fish -
Fri 3/8 Review Day Finish Lab -

Assignments and Deadlines:

Week 8 (Hypothesis Testing)

Date Lecture Lab Reading
Mon 3/11 Hypothesis Testing Hypothesis Testing -
Wed 3/13 Hypothesis Testing cont. Final Project -
Fri 3/15 TBD -

Assignments and Deadlines:

  • No Homework assigned this week
  • Details for class project TBD

SPRING BREAK

Week 9 (Decision Error)

Date Lecture Lab Reading
Mon 4/1 Review Null Distribution
Wed 4/3 Decision Error Hypothesis Testing
Fri 4/5 TBD Hypothesis Testing

Assignments and Deadlines:

Week 10 (Exam 2)

EXAM REMINDERS

Topics: Sampling, bootstrapping, confidence intervals, hypothesis testing, p-values, decision errors

  • In room Noyce 2022
Date Lecture Lab Reading
Mon 4/8 Practice
Solutions to slides
Wed 4/10 Exam 2 😎
Fri 4/12 Multiple Testing Hypothesis Testing

Assignments and Deadlines:

Review your mentors!

Exam #3 Content

Multiple comparisons, difference tests, \(\chi^2\), ANOVA, regression

Week 11 (\(\chi^2\) Tests)

Date Lecture Lab Final Project
Mon 4/15 Difference tests Hypothesis Testing cont.
Wed 4/17 \(\chi^2\) Test Proposal Due 5pm
Fri 4/19 \(\chi^2\) Test
  • Homework 6 due Wed 4/24 at 11:59pm
  • Lab due Monday 4/24 at 11:59pm

Week 12 (ANOVA)

Date Lecture Lab Final Project
Mon 4/22 Class Survey
Wed 4/24 ANOVA ANOVA
Fri 4/26 ANOVA Cont. ANOVA

Week 13 (Regression)

Date Lecture Lab Final Project
Mon 4/29 ANOVA/Regression Regression and Inference
Wed 5/1 Multiple Regression Exploratory Analysis Due 5pm
Fri 5/3 Regression Error

Week 15 (Presentations)

Date Lecture Lab Final Project
Mon 5/13
Wed 5/15 STA-209-05 Presentations @ 9-12
Fri 5/17 STA-209-03 Presentations @ 9-12

Final report due Friday 5/17 at 5pm

Walkthroughs

Shiny App

Null Distribution – helpful for investigating relationship between absolute distance, standard deviation, and sample size for null distributions

Lab Walkthroughs

Posted here are videos walking through the lab content

Lab 5 – dplyr

Lab 6 – Bootstrapping

Lab 8 – ANOVA

Lab 9 – Regression

Homework Walkthroughs

Homework 4

Stat Videos on YT

Here are some content creators on YouTube who I have used in the past to help me understand concepts in statistics and probability. Some of them are more organized than others (i.e., via playlists), but you could always try searching something like “[creator] central limit theorem” to find results

  • Khan Academy (good for everything)
  • Ben Lambert (good for stats)
  • Patrick JMT (good for math, some stats)
  • 3Blue1Brown (my personal favorite, especially his series on linear algebra)

If you have others that you have found useful, let me know and I will add them to the list