STA/OPR 9750, Fall 2018

Basic information

• Classes: MoWe 550pm-705pm, 12-145 VC
• Instructor: Víctor Peña
• Office hours: By appointment, and MoWe noon-2pm, 11-273 VC
• email: victor.pena@baruch.cuny.edu

Handouts & datasets

• Lecture I: Reading in data and basic summaries, plots
  • Handout, Pollution.prn, Pollution.xls.
• Lecture II: Confindence intervals, hypothesis tests, etc.
  • Handout, Survey.xlsx, Pollution.xls.
• Lecture III: Non-parametric tests, contingency tables
  • Handout, depression.csv, tat.csv, xray.csv, zinc_conc.txt, scores.csv.
• Lecture IV: Confidence intervals & Hypothesis testing
  • Slides.
• Lecture V: Correlation & simple linear regression
  • Slides, huswif.csv.
• Lecture VI: More simple linear regression
  • Regression handout, huswif.csv.
• Lecture VII: Diagnostics, influential observations, intro to multiple linear regression
  • Regression handout, Slides, huswif.csv, Animals.csv.
• Lecture VIII: Transformations
  • More regression handout, code, how to interpret log transformations.
• Lecture IX: Transformations, influential observations in multiple linear regression
  • More regression handout,iq.txt, code.
• Lecture X: Categorical predictors
  • Equivalent models, STA500 @ Penn State - Categorical Predictors,evals_sas.csv,evals_code.sas, scores_code.sas, More regression handout.
• Lecture XI: Model building, categorical predictors
  • Model building slides, evals_code.sas,brain_code.sas, More regression handout.
• Lecture XII: Model building, multiple comparisons
  • Model building slides, More regression handout.
• Lecture XIII: Multiple comparisons
  • bloodpressure.txt, hsb2.xlsx, slides
• Lecture XIV: Multicollinearity
  • Slides, LASSO with SAS video,code.
• Lecture XV: Intro to R
  • R, Rstudio, code
• Lecture XVI: HW1 with R
  • code
• Lecture XVII: Lists, linear regression with R
  • liststuff.R, midterms.R, midterms.csv
• Lecture XVIII: Linear regression with R
  • midterms.R, animals.pdf, Animals.csv.
• Lecture XIX: More linear regression with R
• Lecture XX: Categorical predictors, model selection, effect plots
  • Nov14.R
• Lecture XXI: Variable selection, search strategies, ANOVA, Tukey HSD, predictions
  • Nov19.R
• Lecture XXII: Introduction to R markdown
  • Course on Datacamp
• Lecture XXIII: Introduction to ggplot2
  • R markdown, html output
• Lecture XXIV: More ggplot2 and introduction to dplyr
  • R markdown, html output
• Lecture XXV: Joining datasets
  • R markdown, html output
• Lecture XXVI: Tidying datasets
  • R markdown, html output

Assignments

• HW1: Due 09/26
  • pharma.csv, school.csv, drill.csv, scores2.csv.
• HW2: Due 10/25
  • hw2data.zip
• Midterm data
  • salary.csv, treats.csv, midterms.csv, Pollution.xls

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
  • 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 Hadley Wickham
  • Quick-R
  • Cookbook for R
• Other
  • An Introduction to Statistical Learning, by James, Witten, Hastie, and Tibshirani
  • Newsletter of the Cornell Statistical Consulting Unit