# 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.

# Contact

Mikael.VejdemoJohansson@csi.cuny.edu

1S-208

Office hours Monday, Wednesday

# Literature

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 examination in this course focuses on your demonstrating these abilities. The examination comes in three parts:

- An explorative data analysis project.
- An inferential data analysis project.
- A final written exam.

The two data analysis projects will be on parts of the same data set, extracted as random samples keyed to your birthdate. (CUNY ID number?) The final examination will be a written exam, paired with a computer lab practical exam.

For the final examination, there will be questions focused on each of the abilities in the learning goals. For a passing grade you **will** produce complete and correct answers in each of these ability groups, and you **will** produce complete and correct reports for each of the two project components.

## Grade requirements

Each report is graded on a reduced scale of **F** / **D** / **C** / **B** / **A**:

For a failing grade, your report has a major flaw.

For a grade of **D**, your reports will have no more than 4 minor flaws.

For a grade of **C**, your reports will be without minor flaws.

For a grade of **B**, your reports will fulfill everything for **C**, and the criteria for **A** are fulfilled with only minor flaws.

For a grade of **A**, the report will also include *a critical examination component*.

The final exam is pass/fail for the course: a failing result for the final exam is a failing grade for the course. The final grade will be an arithmetic mean of the grades, with:

**D**counting as 1**C**counting as 2**B**counting as 3**A**counting as 4

There will be an opportunity in the final that allow you to raise your grade by up to two minor grade steps: for instance, raise from a B to a B+ or an A-.

To summarize:

Report grades | Exam grade | 1 grade raise | 2 grade raises |
---|---|---|---|

D D | D | C | C |

D C | C | C+ | B- |

D B | C | C+ | B- |

D A | C | C+ | B- |

C C | C | C+ | B- |

C B | C | C+ | B- |

C A | B | B+ | A- |

B B | B | B+ | A- |

B A | B | B+ | A- |

A A | A | A | A |

## 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