SDS/MTH 220 - Fall 2017
  • Syllabus
  • Schedule
  • Resources
    • Helpful Links
    • Seelye Exam Instructions
    • Troubleshooting R Markdown
    • Undergraduate Statistics Project Competition
    • Handouts
    • R quiz study guide
    • Syntax comparison cheatsheet
    • Data wrangling cheatsheet
    • RMarkdown cheatsheet
    • ggplot2 cheatsheet
  • Lectures
    • Notes
    • 01 - Yawning
    • 02 - Data & Sampling
    • 03 - Experimental Design
    • 04 - Center, Shape, and Spread
    • 05 - Bivariate Relationships
    • 06 - Linear Regression
    • 07 - Linear Fit
    • 08 - Outliers, Leverage, and Influence
    • 09 - Multiple Regression
    • 09a - Midterm Review
    • 10 - Randomization Test
    • 11 - Hypothesis Testing
    • 12 - Simulation
    • 13 - Normal Distribution
    • 14 - Confidence Intervals
    • 15 - More on Confidence Intervals
    • 16 - Probability
    • 17 - Conditional Probability
    • 18 - Random Variables
    • 18a - Midterm 2 Review
    • 19 - Inference for a Single Proportion
    • 20 - Difference of two Proportions
    • 21 - Goodness of Fit
    • 22 - Independence
    • 23 - Inference for a Mean
    • 24 - Difference of Two Means
    • 25 - ANOVA
    • 26 - The Bootstrap
    • 25 - Inference for Regression
    • 26 - More Regression
    • Inference overview
    • Which test review
  • Sources
    • 01 - Yawning
    • 02 - Data & Sampling
    • 03 - Experimental Design
    • 04 - Center, Shape, and Spread
    • 05 - Bivariate Relationships
    • 06 - Linear Regression
    • 07 - Linear Fit
    • 08 - Outliers, Leverage, and Influence
    • 09 - Multiple Regression
    • 10 - Randomization Test
    • 11 - Hypothesis Testing
    • 12 - Simulation
    • 13 - Normal Distribution
    • 14 - Confidence Intervals
    • 15 - More on Confidence Intervals
    • 16 - Probability
    • 17 - Conditional Probability
    • 18 - Random Variables
    • 18a - Midterm 2 Review
    • 19 - Inference for a Single Proportion
    • 20 - Difference of two Proportions
    • 21 - Goodness of Fit
    • 22 - Independence
    • 23 - Inference for a Mean
    • 24 - Difference of Two Means
    • 25 - ANOVA
    • 26 - The Bootstrap
    • 25 - Inference for Regression
    • 26 - More Regression
    • Inference overview
  • Labs
    • Labs
    • Introduction to R and RStudio
    • Introduction to Data
    • Simple Linear Regression
    • Confidence Intervals
    • Probability
    • Inference for Categorical Data
    • Normal Distribution
    • Inference for Numerical Data
    • Lab Sources
    • Introduction to R and RStudio
    • Introduction to Data
    • Simple Linear Regression
    • Confidence Intervals
    • Probability
    • Inference for Categorical Data
    • Normal Distribution
    • Inference for Numerical Data
    • Lab Templates
    • Introduction to R and RStudio
    • File -> New File -> R Markdown -> From Template
    • Lab CSS
  • Assignments
    • Homework
    • All Homework Assignments
    • Project
    • Instructions
    • Schedule
    • Proposal
    • Data Appendix
    • Presentation
    • Project peer evaluation
    • Technical Report
    • Sample Data Appendix
    • Sample Writeup 1
    • Sample Writeup 2
    • Group Dynamic
    • Advice

Resources

  • Smith Moodle
  • OpenIntro with Randomization and Simulation
    • PDF of the textbook
    • R packages: openintro and OIdata
    • Click on “Typos and feedback” to send corrections to the authors
    • an online survey
  • RStudio IDE
    • Choose one of two options:
      1. Log on to the Smith College RStudio Server
      2. Download and install RStudio Desktop
        • Download and install R
    • Learn more about R Markdown
      • printable Reference Guide for R Markdown
    • RStudio’s cheatsheets for:
      • R Markdown
      • Data Wrangling with dplyr
      • Data Visualization with ggplot2
    • Packages you should install: mosaic, mosaicData, openintro, OIdata, knitr, markdown
    • Interactive tutorials via swirl
  • Using R with the mosaic package
    • the mosaic package
    • Graphics with mosaic
    • Less Volume, More Creativity
    • Minimal R for Intro Stats: one page handout with R commands
    • mosaic resources
    • A Student’s Guide to R
    • Randomization-based inference using the mosaic package
    • Data Computing at Macalester
  • Quantitative Resources on campus
    • Statistics TAs are available Sunday through Thursday from 7-9 pm in Burton 301
    • visit the Spinelli Center for Quantitative Learning
    • list of single-topic workshops hosted by the Spinelli Center

Created by Ben Baumer, edited by Amelia McNamara.