Assignments STT 3851 - Spring 2022

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Jan 11:

• Go over syllabus.
• Become familiar with the Appstate RStudio server. You will use your Appstate user name and password to log in to the server. You must be registered in the class to access the server.
• Chapter 1 - Introduction to Statistical Learning — Take notes in Rmarkdown

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Jan 13:

• Before class:
  • Read chapter 1 ISLR
  • Watch Opening Remarks and Examples (18:18)
  • Watch Supervised and Unsupervised Learning
• Chapter 1 - Introduction to Statistical Learning — Take notes in Rmarkdown

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Jan 18:

• Before class:
  • Read chapter 2 ISLR
  • Watch Statistical Learning and Regression (11:41)
• Chapter 2 - Statistical Learning — Take notes in Rmarkdown

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Jan 20:

• Before class:

  • Watch Curse of Dimensionality and Parametric Models (11:40)

• Chapter 2 - Statistical Learning — Take notes in Rmarkdown

• In class quiz 01

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Jan 25:

• Before class:
  • Watch Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
• Chapter 2 - Statistical Learning — Take notes in Rmarkdown

Lab 02

• Start: Reproduce Lab: Modified Introduction To R.

• Submit your html file and Rmd file Lab 2, no later than 5 pm Feb 6th.

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Jan 27:

• Chapter 2 - Statistical Learning — Take notes in Rmarkdown

• Continue to work on Chapter 02 Lab (Due Sunday - Feb 6th)

• In class quiz 02

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Feb 01:

• Chapter 2 - Statistical Learning — Take notes in Rmarkdown
• Bias-Variance Trade-Off
• Continue to work on Chapter 02 Lab (Due Sunday - Feb 6th)

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Feb 03:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• Before class:

  • Refer to bookdown: Authoring Books and Technical Documents with R Markdown as needed

• Chapter 02 Lab Due Sunday.

• In class quiz 03

Lab 02

• Submit your html file and Rmd file Lab 2, no later than 5 pm Feb 6th.

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Feb 08:

• Before class:
  • Read pages 59-82 of ISLR
  • Watch Simple Linear Regression and Confidence Intervals (13:01)
• Chapter 3 - Linear Regression — Take notes in Rmarkdown

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Feb 10:

• Before class:

  • Watch Hypothesis Testing (8:24)
  • Watch Multiple Linear Regression and Interpreting Regression Coefficients (15:38)

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• In class quiz 04

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Feb 15:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

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Feb 17:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• In class quiz 05

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Feb 22:

• Before class:
  • Watch Lab: Linear Regression (22:10)
• Chapter 3 - Linear Regression — Take notes in Rmarkdown

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Feb 24:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

Chapter 3 Lab

• Start: Reproduce Lab: Linear Regression.

• Submit your Chapter 3 Lab html file and the Rmd file, no later than 5 pm March 24th.

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March 01:

• Before class:

  • Read pages 82-120 ISLR
    • Watch Model Selection and Qualitative Predictors (14:51)

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

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March 03:

• Before class:

  • Watch Interactions and Nonlinearity (14:16)

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• In class quiz 06

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Mar 08:

• Spring Break- No classes

Mar 10:

• Spring Break- No classes

Mar 15:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

Reminder: Chapter 3 Lab

• Keep working on: Reproduce Lab: Linear Regression.

• Submit your Chapter 3 Lab html file and the Rmd file, no later than 5 pm March 24th.

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Mar 17:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• Exam 1 is Tuesday next week!

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Mar 22:

Project 01

• Project 1.

• Submit your html and Rmd files no later than 5 pm April 5th.

• In class quiz 07

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Mar 24:

• In class Exam 1
• Exam Part I
• Exam Part II

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Mar 29:

• Chapter 5 - Resampling Methods — Take notes in Rmarkdown

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Mar 31:

• Chapter 5 - Resampling Methods — Take notes in Rmarkdown

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April 05:

• Chapter 5 - Resampling Methods — Take notes in Rmarkdown

• In class quiz 08

• Project 1 due today

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April 07:

• Chapter 6 - Linear Model Selection and Regularization — Take notes in Rmarkdown

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April 12:

• Chapter 6 - Linear Model Selection and Regularization — Take notes in Rmarkdown

• In class quiz 09

Chapter 5 Lab

• Start: Reproduce Chapter 5 Lab.

• Submit your Chapter 5 Lab html file and the Rmd file, no later than 5 pm April 22th.

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April 14:

• Chapter 6 - Linear Model Selection and Regularization — Take notes in Rmarkdown

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April 19:

• Chapter 6 - Linear Model Selection and Regularization — Take notes in Rmarkdown

• In class quiz 10

• Project 2: Start Here

• Turn in Project 2 Predictions- NLT May 3rd, 10:00 am

• Send an email to the instructor using a subject of “House Data Predictions” with an attached vector (csv file) of your predictions using the data set housedataT.csv. Note: your vector should contain 4229 predictions.

• Turn in Project 2 - NLT May 06th, 5 pm

• Make sure a single .Rmd and a single .html with the sections outlined in the Example Paper is in your Project2 emailed to me.

• Project 2 Leaderboard

Reminder:

• Exam 2, Tuesday next week

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April 21:

• Exam 2

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• Auto Data .txt
• Auto Data .csv

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