STA/OPR 9750, Spring 2019

Basic information

• Classes: MoWe 730pm-845pm, 10-135 VC
• Instructor: Víctor Peña, office 11-273 VC
• Office hours: By appointment
• email: victor.pena@baruch.cuny.edu
• Syllabus: See Blackboard

Course materials

Week 1: Introduction to SAS

• Handout
• Data: Pollution.prn, Pollution.xls, hsb2.csv, speed_gender_height.csv
• Code: Lecture 1, Lecture 2
• Getting SAS: SAS university edition (you can work offline), CUNY Virtual Desktop

Week 2: Summarizing data with SAS

• Handout
• Data: hsb2.csv, speed_gender_height.csv, midterms.csv
• Code: Lecture 3, Lecture 4
• Links:
  • Anyone can learn PROC TABULATE, Introduction to PROC TABULATE
  • Introduction to SGPLOT
  • LOESS video

Week 3: Basic data analysis with SAS

• Data: Pollution.xls, midterms.csv, before.csv, after.csv
• Code: Lecture5.sas, Lecture6.sas
• Exercises

Week 4: Basic hypothesis testing with SAS

• Data: drug.csv, 2drugs.csv
• Slides
• Code: Lecture7.sas

Week 5: Hypothesis testing and correlation

• Data: depression.csv
• Slides: Lecture 8, Lecture 9
• Code: Lecture8.sas, Lecture9.sas

Week 6: Simple linear regression with SAS

• Handout: Simple linear regression with SAS
• Data: galton.csv, huswif.csv, animals.csv
• Slides: SLR.pdf
• Code: Lecture10.sas
• Video about Galton

Week 7: Multiple linear regression with SAS

• Handouts: Simple linear regression with SAS, Multiple linear regression with SAS
• Data: iq.txt, animals.csv
• Slides: Lecture 11, Lecture 12
• Code: Lecture11.sas, Lecture12.sas
• How to interpret log transformations

Week 8: More regression with SAS

• Handouts: Multiple linear regression with SAS
• Data: iq.txt, speed_gender_height.csv, evals_sas.csv, hsb2.csv.
• Code: Lecture13.sas, Lecture14.sas

Week 9: Introduction to R

• Handouts: Introduction to R, Some hypothesis tests with R
• Data: hsb2.csv, pharma.csv
• Code: IntrotoR.R, basicstats.R
• Rmarkdown: IntrotoR.Rmd, basicstats.Rmd

Week 10: Plotting and linear regression with R

• Handouts: Introduction to ggplot2, Linear regression with R
• Data: operation.csv, nyc.csv, nyctest.csv
• Code: simpsons.R, introggplot2.R, introregression.R
• Rmarkdown: introggplot2.Rmd, introregression.Rmd

Week 11: Plotting and manipulating data with R

• Handouts: Linear regression with R, More ggplot2 and introduction to dplyr, Tidying datasets
• Data: nyc.csv, nyctest.csv
• Code: introregression.R
• Rmarkdown: introregression.Rmd, More ggplot2 and introduction to dplyr, Tidying datasets

Week 12: Combining data with R and intro to geospatial data

• Handout: Combining datasets
• Data: corvallis.csv, ward_sales.csv
• Code: spatial.R
• Rmarkdown: combining.Rmd

Week 13: More geospatial data with R and logistic regression

• Data: corvallis.csv, NFL field goal data (description)
• Code: spatialColor.R, may8.R

Week 14: More logistic regression with R

• Data: NFL field goal data (description), Interfaith dating (description)
• Code: may13.R, may15.R

Resources

• Basic statistics
  • Khan Academy - Probability and Stats
  • onlinestatbook
  • Shafer and Zhang
  • Seeing Theory @ Brown
• Regression
  • STA501 @ Penn State
  • Practical regression and ANOVA with R, by Faraway
• SAS
  • Color guide SAS
  • Data flair: A Complete Guide to Mastering SAS
  • Getting started with SAS @ UT Austin
  • Statistical inference with SAS @ UT Austin
  • STA480 @ Penn State
  • STA481 @ Penn State
  • STA482 @ Penn State
• R
  • Applied stats with R, by David Dalpiaz
  • R for Data Science, by Grolemund and Wickham
  • Quick-R
  • Cookbook for R
  • Colors in R, by Tian Zheng @ Columbia
  • ggplot2 website
  • ggplot2 cheatsheet
  • Data visualization, by Kieran Healy
• Other
  • An Introduction to Statistical Learning, by James, Witten, Hastie, and Tibshirani
  • Newsletter of the Cornell Statistical Consulting Unit
  • Markdown tutorial
  • Using github and R