University of Michigan, Fall 2017

- Lectures:
B844 East Hall, MW 2:30–4pm

- Labs:
G444C Mason Hall, F 9–10am, 10–11am

G444B Mason Hall, F 2–3pm- Instructor:
- GSI's:
- Office hours:
271 West Hall, Tu 5:30–7pm

Science Learning Center, MW 1–2:30pm, Tu 3–4:30pm

Please see the syllabus for other course information.

Date | Topics | References |

Sep 6 | supervised learning, linear regression | |

Sep 8 | linear algebra review | linalg.PDF |

Sep 11 | ordinary least squares (OLS) | |

Sep 13 | OLS as MLE, hypothesis testing | |

Sep 15 | R bootcamp | ISL §2.3 |

Sep 18 | hypothesis testing under normality | |

Sep 20 | the \(t\)-test, the bootstrap | bootstrap.PDF |

Sep 22 | linear regression lab | ISL §3.6 |

Sep 25 | bootstrap confidence intervals | |

Sep 27 | classical linear model | |

Sep 29 | qualitative predictors | ISL §3.3.1, §3.6.6 |

Oct 2 | OLS as MoM, geometry of least squares | |

Oct 4 | finite-sample properties of OLS | |

Oct 6 | midterm I review | |

Oct 9 | Gauss-Markov theorem | |

Oct 11 | midterm I | |

Oct 13 | bootstrap lab | ISL §5.3.4 |

Oct 18 | convergence of random variables | |

Oct 20 | midterm I recap |

ISL refers to the textbook *An Introduction to Statistical Learning*.

- linreg1.PDF
linear regression, OLS, hypothesis testing under normality

- linreg2.PDF
classical linear model, finite-sample properties of OLS

- linreg3.PDF
convergence of random variables, asymptotic properties of OLS

- mvn.PDF
the multivariate Gaussian/normal (MVN) distribution

There are plenty of R resources online. The RStudio online learning page is a good place to start. If you find a resource that is particularly helpful, please let me know so that I may share it with the class.

Due date | Assignment | Data files |

Sep 15 | pset0.PDF | |

Sep 15 | plab1.PDF | |

Sep 22 | plab2.PDF | |

Sep 29 | pset1.PDF | Boston.CSV, n88_pol.CSV |

Sep 29 | plab3.PDF | |

Oct 13 | plab4.PDF | |

Oct 18 | pset2.PDF | penn.CSV |

Please check Canvas for solutions to the problem sets.