Welcome to MTH 214

We will be covering applied statistics, and the statistical software platform R. The course meets Mondays and Wednesdays, 14.30-16.25. Your instructor is Prof. Mikael Vejdemo-Johansson. These webpages form the course syllabus, you'll find information on the course schedule, the structure of class meetings, grading policies and structure of this course's examination.

By using the menu to the right, you can reach pages with more details on the course schedule (including current and upcoming homework assignments), criteria and details on the essays, and links to places you can search for datasets.


Office hours Monday, Wednesday


We will be using Introduction to the Practice of Statistics 8th ed (Amazon: rent $35, buy used $35, buy new $132), The R Software (when accessed through the campus library: $0 ebook, $25 paperback) and ggplot2 (when accessed through the campus library: $0 ebook, $25 paperback).

Learning goals and examination

After you finish this course, you will:

  • Be able to perform explorative statistical analysis on a new dataset, including identifying variables, reading and understanding a data codebook, produce statistical summaries of variables, produce descriptive plots of single variables, and relating sets of variables to each other.
  • Be able to perform inferential statistical analysis on a new dataset, including one-tailed and two-tailed hypothesis testing of means and proportions, linear regression and ANOVA.
  • Be able to construct and suggest experiment designs to produce data that could answer a given data-driven inquiry.
  • Be able to interpret, and critically examine results of these analyses.
  • Be able to communicate your analyses clearly, correctly and coherently in text and in speech.

The course is graded on five components:

  1. 40% An explorative data analysis project
  2. 40% An inferential data analysis project
  3. 10% Labs
  4. 5% Homework
  5. 5% An non-mandatory final written exam

The two data analysis projects will be on parts of the same data set, extracted as random samples keyed to your CUNY ID number. The final examination will be a written exam.

Report grading rubric

As minor flaws count:

  • Reproducibility issues: failing to provide source code for a compilable and working R-markdown report
  • Language/legibility issues: spelling, grammar, disposition, excessive verbosity
  • Completeness issues: skipping/missing parts of the analyses or parts of the data; using the wrong slice from the dataset
  • Motivation issues: failing to coherently and completely motivate analysis choices -- data included, methods used, presentation means chosen

As major flaws (failing the report and thereby the course):

  • Erroneous claims
  • Missing analysis sections
  • Missing report
  • Missing a deadline

A failed report can be resubmitted, and will earn half credit.