Assignments STT 3851 - Fall 2023

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Aug 22:

• 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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Aug 24:

• 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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Aug 29:

• 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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Aug 31:

• 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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Sep 5:

• 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 Sep 14th.

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Sep 07:

• Chapter 2 - Statistical Learning — Take notes in Rmarkdown

• Continue to work on Chapter 02 Lab (Due Thursday - Sep 14th)

• In class quiz 02

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Sep 12:

• Chapter 2 - Statistical Learning — Take notes in Rmarkdown
• Bias-Variance Trade-Off

Lab 02

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

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Sep 14:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• Before class:

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

• In class quiz 03

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Sep 19:

• 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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Sep 21:

• 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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Sep 26:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

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Sep 28:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• In class quiz 05

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Oct 3:

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

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Oct 05:

• 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 Oct 24th.

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Oct 10:

• 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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Oct 12:

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Oct 17:

• Fall Break- No classes

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Oct 19:

• Before class:

  • Watch Interactions and Nonlinearity (14:16)

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• In class quiz 06

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Oct 24:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

Reminder: Chapter 3 Lab

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

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Oct 26:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

• Exam 1 is Thursday next week!

• In class quiz 07

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Oct 31:

• Chapter 3 - Linear Regression — Take notes in Rmarkdown

Project 01

• Project 1.

• Submit your html and Rmd files no later than 5 pm Nov 9th.

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Nov 2:

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

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Nov 7:

• Chapter 5 - Resampling Methods — Take notes in Rmarkdown

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Nov 9:

• Distribution of the Exam 1 Grades

• Chapter 5 - Resampling Methods — Take notes in Rmarkdown

• In class quiz 08

Project 01

• Project 1.

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

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Nov 14:

• Chapter 5 - Resampling Methods — Take notes in Rmarkdown

Chapter 5 Lab

• Start: Reproduce Chapter 5 Lab.

• Submit your Chapter 5 Lab html file and the Rmd file, no later than 5 pm Nov 21st

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Nov 16:

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

• In class quiz 09

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Nov 21:

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

Chapter 5 Lab

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

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Nov 23:

• State Hiliday- No classes

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Nov 28:

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

• In class quiz 10

Reminder:

• Exam 2, next week

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Nov 30:

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

• Project 2: Start Here

• Turn in Project 2 Predictions- NLT Dec 1st, 5:00 pm

• 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 Dec 8th, 5:00 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.

• Also turn in the all the model you have tried as a single .Rmd and a single .html file. Section names should read for example: Model 1: Forward selection model. A brief description of the model should be included as well.

• Project 2 Leaderboard

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Dec 5:

• Exam 2

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• Auto Data .txt
• Auto Data .csv

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