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Last updated: Wed Nov 20 2024, 08:02

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 weโ€™ve reached that point in the course.

Math lab hours

Course Materials

Exam #1 Content

Week 0 (Intro)

Date Lecture Lab Reading
Mon 8/26 - - -
Wed 8/28 - - -
Fri 8/30 Syllabus - -

Assignments and Deadlines:

  1. Download R
  2. Download R Studio
  3. (Windows only, maybe) Download Rtools44

Week 1 (Data Summaries and Visualization)

Date Lecture Lab Reading
Mon 9/2 Intro to R Intro to R Packages
Wed 9/4 Introduction to Statistics ggplot2
Fri 9/6 Data Summaries โ€“ Visualization Continue ggplot

Assignments and Deadlines:

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 2 (Tables and Numerical Summaries)

Date Lecture Lab Reading
Mon 9/9 Numerical Summaries Continue ggplot lab
Wed 9/11 Numerical Summaries cont. Table Lab
Fri 9/13 Tables Cont. Table Lab

Assignments and Deadlines:

  • Lab 2 due Fri Sept 13 at 10pm
  • Homework 2 due Fri Sept 20 at 10pm
  • Bonus lab for ggplot2 for scales, axes, colors, and themes

Week 3 (Odds and Z-Scores)

Date Lecture Lab Reading
Mon 9/16 Odds dplyr
Wed 9/18 Z-Scores and Correlation Cont. dplyr
Fri 9/20 Regression Regression and Correlation

Assignments and Deadlines:

ASSIGNED

DUE

  • Homework 2 due Friday, Sept 20 at 10pm
  • Table lab due Wednesday, Sept 18 at 10pm

Week 4 (Regression)

Date Lecture Lab Reading
Mon 9/23 Regression (Categorical Predictors) Cont. Regression Lab
Wed 9/25 Regression Multivariate Cont. Regression Lab
Fri 9/27 Review Day

Assignments and Deadlines:

DUE

  • Homework 3 due Friday, Sept 27 at 10pm
  • dplyr lab due Friday, Sept 27 at 10pm
  • Regression lab due Friday, Sept 27 at 10pm

Exam #2 Content

Week 5 (Study Design)

Date Lecture Lab Reading
Mon 9/30 Exam 1 ๐Ÿ˜Ž
Wed 10/2 R day dplyr and ggplot
Fri 10/4 Study Design cont Reading

Assignments and Deadlines:

Extra tentative from this point on

Week 7 (Sampling Distributions)

Date Lecture Lab Final Project
Mon 10/14 Sampling Distribution Fish 1 and Fish 2
Wed 10/16 Normal Approximation and Intervals
Fri 10/18 CLT Worksheet CLT Lab Overview

Assignments and Deadlines:

FALL BREAK

(yay)

Assignments and Deadlines:

Week 8 (Sampling Distributions)

Date Lecture Lab Final Project
Mon 10/28 t-distribution Continue last week lab
Wed 10/30 t-distribution t-Dist and Bootstrap
Fri 11/01 Bootstrapping Lab

Assignments and Deadlines

  • CLT lab due to the course mentor at the start of class 11/01
  • Homework 6 due 11/08 at 10pm
  • Lab 8 due 11/06 at 10pm

Week 9 (Hypothesis Testing)

Date Lecture Lab Final Project
Mon 11/4 Hypothesis Testing Hypothesis Testing
Wed 11/6 ๐Ÿ‘ฝ Tools
Fri 11/8 Null Distributions Null Distribution Worksheet

Assignments and Deadlines:

Exam #3 Content

Week 10 (Strength of Evidence and Decision Error)

Date Lecture Lab Final Project
Mon 11/11 Strength of Evidence
Wed 11/13 Exam 2 ๐Ÿ˜Ž
Fri 11/15 Decision Error Final Project

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

Date Lecture Lab Final Project
Mon 11/18 Difference Tests Hypothesis Testing cont.
Wed 11/20 \(\chi^2\) test (Goodness of Fit)
Fri 11/22 \(\chi^2\) test (Independence)

Week 12

Date Lecture Lab Final Project
Mon 11/25
Wed 11/27
Fri 11/29 Thanksgiving

Week 13

Date Lecture Lab Final Project
Mon 12/2 Multiple Regression
Wed 12/4 Regression Extras
Fri 12/6 Regression Error

Week 14

Date Lecture Lab Final Project
Mon 12/9 Project Work Day
Wed 12/11 Review
Fri 12/13 Review

Week 15 (Final Exam Week)

Exams in Noyce 2402

  • STA-209-01 (8:30-9:50) โ€“ Tuesday, Dec 17 9am-12pm
  • STA-209-02 (10:00-11:20) โ€“ Wednesday, Dec 18 9am-12pm

Two options, you can pick day of:

  • Standard Exam 3 written to be an 80 minute exam
  • Comprehensive Exam 3, written to be 160 minute exam
    • Replace lowest exam grade for Exam 1 or Exam 2