STA-209 - Applied Statistics (Spring 2024)

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Welcome to the course website for Sta-209-04 (Applied Statistics). You can find the course syllabus linked below:

• Click here to view the course syllabus

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

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

Course Materials

Most class meetings involve both lecture and lab components. Partners will be assigned for each lab. Labs will generally be due 1-2 class meetings after they are assigned, while homework is consistently due every Friday at midnight (with some exceptions surrounding exams).

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Exam #1 Content

Week 1:

Date	Lecture	Lab
Mon 1/22	Data Basics	Introduction to R
Wed 1/24	Data Visualization	Data Visualization (part 1)
Fri 1/26		Data Visualization (part 2)

Assignments and Deadlines (week 1):

• The Introduction to R and Data Visualization labs
  • Due Monday 1/29 Wednesday 1/31 at 11:59pm (via P-web)
• Homework 1
  • Due Friday 1/26 Tuesday 1/30 at 11:59pm (via P-web)

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Week 2:

Date	Lecture	Lab
Mon 1/29		Extra credit lab
Wed 1/31	Descriptive statistics (categorical data)	R Markdown and Tables
Fri 2/2	More on contingency tables

Assignments and Deadlines (week 2):

• Week 2 labs (R Markdown and Tables, and Descriptive Statistics)
  • Due Monday 2/5 at 11:59pm (via P-web)
• Homework #2
  • Due Friday 2/2 at 11:59pm (via P-web)

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Week 3:

Date	Lecture	Lab
Mon 2/5	Descriptive statistics (quantitative outcomes)	Summarizing data with dplyr
Wed 2/7	Descriptive statistics (correlation)
Fri 2/9		Correlation and regression

Assignments and Deadlines (week 3):

• Week 3 labs (dplyr, correlation and regression)
  • Due Monday 2/12 at 11:59pm (via P-web)
• Homework #3
  • Due Friday 2/9 at 11:59pm (via P-web)

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Week 4:

Date	Lecture	Lab
Mon 2/12	Stratification	Multivariate Relationships
Wed 2/14		Multivariable regression (part 1)
Fri 2/16		Multivariable regression (part 2)

• Week 4 labs (multivariate relationships, and multivariable regression parts 1 and 2)
  • Due Monday 2/19 at 11:59pm (via P-web)
• Homework #4
  • Due Friday 2/16 at 11:59pm (via P-web)

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Week 5:

Date	Lecture	Lab
Mon 2/19	Exam 1 Review — sample solution
Wed 2/21	Exam 1	Exam 1
Fri 2/23	Sampling from a Population	Sampling Variability

• Exam 1 will take place in Noyce 2022 at 1pm on Wednesday 2/21
  • You may use a basic calculator (but not one that can store data).
  • A practice exam is found here
    • The sketch solution is found here
  • A study guide is found here
• There is no homework assignment this week due to Exam 1.
• Lab 9 (Sampling variability) will not be due until Monday 3/4.

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Exam #2 Content

The sampling from a population lecture and lab from Friday 2/23 will be a topic on Exam 2, along with the following content:

Week 6:

Date	Lecture	Lab
Mon 2/26	Interval Estimation	Bootstrapping
Wed 2/28		Bootstrapping (cont.)
Fri 3/1	Normal Approximations

• Week 6 labs (bootstrapping) and Lab 9 from Friday 2/23
  • Due Monday 3/4 at 11:59pm (via P-web)
• Homework #5
  • Due Friday 3/1 at 11:59pm (via P-web)

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Week 7:

Date	Lecture	Lab
Mon 3/4	Student’s \(t\)-distribution
Wed 3/6		Confidence Intervals in R
Fri 3/8		Confidence Intervals in R (cont.)

• Week 7 labs (confidence intervals in R)
  • Due Monday 3/11 at 11:59pm (via P-web)
• Homework #6
  • Due Friday 3/8 at 11:59pm (via P-web)

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Week 8:

Date	Lecture	Lab
Mon 3/11	Null hypotheses, \(p\)-values, and decisions
Wed 3/13		Randomization Tests
Fri 3/15	Class Project	Class Project

• Week 8 lab (Randomization Tests)
  • Due Friday 3/15 at 11:59pm (via P-web)
• Homework #7
  • Due Friday 3/15 at 11:59pm (via P-web)

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Week 9:

Date	Lecture	Lab
Mon 4/1	Normal Approximations for Hypothesis Testing
Wed 4/3		Hypothesis Testing in R
Fri 4/5		Hypothesis Testing in R (cont.)

• Week 9 lab (Hypothesis Testing in R)
  • Due Monday 4/8 at 11:59pm (via P-web)
• Homework #8
  • Due Friday 4/5 at 11:59pm (via P-web)

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Week 10:

Date	Lecture	Lab
Mon 4/8	Exam 2 Review
Wed 4/10	Exam 2	Exam 2
Fri 4/12	Outliers, transformations, and non-parametric tests

• Exam 2 is planned for Wednesday 4/10 during class (1-2:20pm)
  • Practice exam
    • The sketch solution is found here
  • Study guide
    • Q and A from the class survey
• There is no homework assignment this week due to Exam 2

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Exam #3 Content

Week 11:

Date	Lecture	Lab
Mon 4/15	Chi-squared Testing
Wed 4/17	Chi-squared Testing (cont)	Chi-squared Testing in R
Fri 4/19		Chi-squared Testing in R (cont)

• Week 11 lab (Chi-Squared Testing in R)
  • Due Monday 4/22 at 11:59pm (via P-web)
• Homework #9
  • Due Friday 4/19 at 11:59pm (via P-web)

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Week 12:

Date	Lecture	Lab
Mon 4/22	Analysis of Variance (ANOVA)
Wed 4/24		Analysis of Variance in R
Fri 4/26	Work on Project	Work on Project

• Week 12 lab (ANOVA in R)
  • Due Monday 4/29 at 11:59pm (via P-web)
• Homework #10
  • Due Friday 4/26 at 11:59pm (via P-web)
• Project exploratory analysis due Friday 4/26 at 11:59pm, submit an R Markdown file and the data it relies upon via P-web (or email, if necessary)

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Week 13:

Date	Lecture	Lab
Mon 4/29	Hypothesis Testing for Regression Models
Wed 5/1		Hypothesis Testing for Regression Models in R
Fri 5/3		Hypothesis Testing for Regression Models in R (cont.)

• There is no homework assignment this week to allow you additional time to work on your project and prepare for Exam 3
• Lab 16 is due Monday 5/6 at 11:59pm

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Week 14:

Date	Lecture	Lab
Mon 5/6	Project Presentations (part 1)
Wed 5/8	Project Presentations (part 2)
Fri 5/10	Exam 3	Exam 3

• Exam 3 is planned for Friday 5/10 during class. The exam is not intended to be cumulative, but since we’ve continued to focus on hypothesis testing you should expect conceptual questions about this topic.

  • Practice exam
    • The sketch solution is found here
  • Study guide

• There is no homework assignment this week due project presentations and Exam 3

• Note: Project reports are due Friday 5/17 at 5pm. No late submissions will be accepted as per college policies.

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Recommended Readings

The chapters/materials listed below are resources that I recommend you read during the week provided in order to deepen your understanding of course topics and expose yourself to additional perspectives. It has been my observation that students who complete these readings have been the most successful.

• Week 1 (1/22 - 1/26):
  • IMS Ch 1.2, 1.3, 4.1, 4.2, 4.6, 5.1, 5.3, 5.5 (focus on the types of graphs discussed in Ch 4/5)
• Week 2 (1/29 - 2/2)
  • IMS Ch 1.1, 4.3, 5.2, 5.4, 5.5, 5.6 (focus on understanding numerical summaries and tables)
• Week 3 (2/5 - 2/9)
  • IMS Ch 5.5, 5.6, 7.1, 7.2, 7.3
• Week 4 (2/12 - 2/16)
  • IMS Ch 7.1, 7.2, 8.1, 8.3
• Week 5 (2/19 - 2/23)
  • None
    
• Week 6 (2/26 - 3/1)
  • IMS Ch 12.1, 12.2, 12.3, 13.1, 13.2
• Week 8 (4/1 - 4/5)
  • IMS Ch 13.5, 16.2.4, 17.3.3, 19.2.1, 19.2.5
• Week 11 (4/15 - 4/19)
  • IMS Ch 18.1, 18.2
• Week 12 (4/22 - 4/26)
  • IMS Ch 22.1, 22.2, 22.23
• Week 13 (4/29 - 5/2)
  • IMS Ch 24.1, 24.4

Project

Information on the course project is found below:

• This link contains the official assignment description and guidelines.
• By Friday 4/26 an exploratory analysis of your final data set is due. See the assignment description for additional details.

Exams

Information about exams (including dates and study materials) will be posted here.

• Exam 1: Planned for Wednesday 2/21 during class (1-2:20pm) in Noyce 2022
  • Practice exam
  • Study guide
  • Solutions to the practice exam will be posted Monday 2/19
• Exam 2: Planned for Wednesday 4/10 during class (1-2:20pm) in Noyce 2022
  • Practice exam
  • Study guide
  • Solutions to the practice exam will be posted Monday 4/8
• Exam 3: Planned for Friday 5/10 during class (1-2:20pm)
  • Practice exam
  • Study guide
  • Solutions to the practice exam will be posted Monday 5/6

Unless otherwise indicated, you are responsible for the content of all lectures, labs, and homework on the corresponding exam.