Assignments STT 3830 - Fall 2019

Aug 19:

• 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.
• Sign-up for free accounts on GitHub.
• When you register for a free individual GitHub account, request a student discount to obtain a few private repositories as well as unlimited public repositories. Please use something similar to first_name_last_name as your username when you register with GitHub. For example, my username on GitHub is alanarnholt. If you have a popular name such as John Smith, you may need to provide some other distinquishing characteristic in your username.
  

• Once you have a GitHub account, send an email to arnholtat@appstate.edu with a Subject line of STT3830 - GitHub Username, and tell me in the body of your email your first name, last name, and your GitHub username.

• Homework:
  • Watch this video (14 minutes) which provides some background on asking questions.
    
  • Read this stackoverflow thread details how to create a minimal R reproducible example.
  • Read How To Ask Questions The Smart Way by Eric Raymond and Rick Moen and heed their advice.
  • Read Getting used to R, RStudio, and R Markdown

Aug 21:

• In class lecture: Review of one and two sample inference, permutation tests, and bootstrap procedures — take notes
• Statistics Review
• Link to Day One Activity and Code

• Link to Day two Activity and Code

  Most GitHub Classroom assignments due dates are 5 p.m. Friday.
• Homework: Watch the following Videos:
  • Phillip Guo’s Unix-like command line tutorials (Four tutorials about 10 minutes each)
  • Phillip Guo’s Basic Git and Github tutorials (Three tutorials about 10 minutes each)
  • Clone a GitHub Repo with RStudio (Video) (5:05)

Aug 23:

• Before class watch the following Videos:
  • Phillip Guo’s Unix-like command line tutorials (Four tutorials about 10 minutes each)
  • Phillip Guo’s Basic Git and Github tutorials (Three tutorials about 10 minutes each)
  • Clone a GitHub Repo with RStudio (Video) (5:05)
• Before class read Steps for downloading and editing assignments from GitHub Classroom
• In class lecture: Review of one and two sample inference — take notes
• Statistics Review
• • Link to Day two Activity and Code
• Homework:
  • Read Chapters 1-2 of bookdown: Authoring Books and Technical Documents with R Markdown
  • Watch Lab: Introduction to R (14:12)
  • Reproduce Modified Introduction to R (Lab1). Accept assignment from GitHub Classroom—link on ASULEARN. Due Aug 30

Aug 26:

• Before class:
  • Read/review Getting used to R, RStudio, and R Markdown
  • Read the Git and GitHub chapter from Hadley Wickham’s book R Packages
• Follow the directions from Happy Git and GitHub for the useR to Introduce yourself to Git, Connect to GitHub, and Cache credentials for HTTPS.
• Read/review Chapters 1-2 of bookdown: Authoring Books and Technical Documents with R Markdown

Aug 28:

• In class lecture: Review of one and two sample inference, permutation tests, and bootstrap procedures — take notes
• Statistics Review
• Use an avatar or picture on your GitHub account

Aug 30:

• Overplotting Examples and possible Solutions

• Due TODAY:
  • Reproduce Modified Introduction to R (Lab1). Accept assignment from GitHub Classroom—link on ASULEARN. Due Aug 30

• Homework: Complete Data Camp Introduction to the Tidyverse — Due Sep 6

Sep 2: No Class - State Holiday

Sep 4:

• Homework: Complete Data Camp Introduction to the Tidyverse — Due Sep 6

Sep 6:

• Due TODAY: Data Camp Introduction to the Tidyverse — Due Sep 6

Sep 9:

• Homework:
  • Data Camp Experimental Design in R - Due Sep 13
  • Reproduce Data Camp Experimental Design in R (DataCamp1) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Sep 20

Sep 11:

• Go over Chapter 2 Graphs

Sep 13:

• Work Day
• Due TODAY: Data Camp Experimental Design in R - Due Sep 13
• Reproduce Data Camp Experimental Design in R (DataCamp1) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Sep 20

Sep 16:

• TBD

Sep 18:

• TBD

Sep 20:

• Due TODAY: Reproduce Data Camp Experimental Design in R (DataCamp1) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Sep 20

Sep 23:

• Before class:
  • Read pages 59-82 of ISLR
  • Watch Simple Linear Regression and Confidence Intervals (13:01)
  • Watch Hypothesis Testing (8:24)
  • Watch Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
• Discuss Chapter 2 and 3 material
• Homework:
  • Complete Data Camp Correlation and Regression - Due Sep 27
  • Reproduce Data Camp Correlation and Regression (DataCamp2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 4

Sep 25:

• Discuss Chapter 2 and 3 material
• Homework:
  • Complete Data Camp Correlation and Regression - Due Sep 27
  • Reproduce Data Camp Correlation and Regression (DataCamp2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 4

Sep 27:

• Discuss Chapter 2 and 3 material
• Homework:
  • Due TODAY: Complete Data Camp Correlation and Regression - Due Sep 27
  • Reproduce Data Camp Correlation and Regression (DataCamp2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 4

Sep 30:

• Discuss Chapter 2 and 3 material
• Homework: Reproduce Data Camp Correlation and Regression (DataCamp2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 4
• Code to help with the production of Figure 6.1

Oct 2:

• Discuss Chapter 2 and 3 material
• Homework: Reproduce Data Camp Correlation and Regression (DataCamp2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 4

Oct 4:

• Due TODAY: Reproduce Data Camp Correlation and Regression (DataCamp2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 4

Oct 7:

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

• Discuss Chapter 2 and 3 material

• Homework:
  • Reproduce Lab: Linear Regression (Lab2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 11
  • Data Camp Multiple and Logistic Regression - Due Oct 18
  • Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 9:

• Before class:
  • Review pages 82-120 ISLR
  • Watch Interactions and Nonlinearity (14:16)
  • Watch Lab: Linear Regression (22:10)

• Discuss Chapter 2 and 3 material

• Homework:
  • Reproduce Lab: Linear Regression (Lab2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 11
  • Data Camp Multiple and Logistic Regression - Due Oct 18
  • Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 11:

• Before class: Review pages 82-120 ISLR

• Discuss Chapter 2 and 3 material

• Homework:
  • Due TODAY: Reproduce Lab: Linear Regression (Lab2) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 11
  • Data Camp Multiple and Logistic Regression - Due Oct 18
  • Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 14 Fall Break

Oct 16:

• Discuss Chapter 2 and 3 material

• Homework:
  • Data Camp Multiple and Logistic Regression - Due Oct 18
  • Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 18:

• Discuss Chapter 2 and 3 material

• Homework:
  • Due TODAY: Data Camp Multiple and Logistic Regression - Due Oct 18
  • Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 21:

• Discuss Chapter 2 and 3 material

• Homework: Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 23:

• Before class:
  • Read the article Modeling Home Prices Using Realtor Data.
  • The data set can be downloaded from http://www.amstat.org/publications/jse/datasets/homes76.dat.txt, while the code book is available from http://www.amstat.org/publications/jse/datasets/homes76.txt
  • Project 1—Accept assignment from GitHub Classroom—link on ASULEARN - Due Nov 8

• Discuss Chapter 2 and 3 material

• Homework: Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 25:

• Discuss Chapter 2 and 3 material

• Due TODAY: Reproduce Data Camp Multiple and Logistic Regression (DataCamp3) - Accept assignment from GitHub Classroom—link on ASULEARN - Due Oct 25

Oct 28:

• TBD

Oct 30:

• TBD

Nov 1:

• TBD

Nov 4:

• TBD

Nov 6:

• TBD

Nov 8:

• Due TODAY: Project 1—Accept assignment from GitHub Classroom—link on ASULEARN - Due Nov 8

Nov 11:

• Before class:
  • Watch Linear Model Selection and Best Subset Selection (13:44)
  • Forward Stepwise Selection (12:26)
  • Backward Stepwise Selection (5:26)

Start Project 2—InClass Kaggle competition

• Example Template Directory
• Use the housedata.csv file - See King County Housing Data
  
• See ASULEARN for InClass prediction competition link.

Nov 13:

• TBD

Nov 15:

• TBD

Nov 18:

• TBD

Nov 20:

• Misc Issues

Nov 22:

• TBD

Nov 25:

• TBD

Nov 27-29: No Class — State Holiday

Dec 2:

• LAST DAY to submit new model for InClass kaggle competition

• Work on presentation

Dec 9:

• Final presentations 11-1:30; WA 303