Week | Topic | Lecture Note |
---|---|---|

1 | Course overview | Week1-1 |

Exploratory data analysis (EDA) | Week1-2 | |

2 | Statistical inference | Week2-1 |

Common distributions: normal, binomial, poisson | Week2-2 | |

3 | Estimate of population proportion | Week3-1 |

Hypothesis testing for a population proportion | Week3-2 | |

4 | Bootstrapping | Week4-1 |

Type I & II error and Power | Week4-2 | |

5 | Bootstrap hypothesis testing and Normal approximation | Week5-1 |

Maximum Likelihood Estimator (MLE) for Normal, Asymptotics, and Central Limit Theorem (CLT) | Week5-2 | |

6 | Comparison of two means, nonparametric | |

Type I error and power | ||

The t-test and its nonparametric alternatives | ||

A case study: data cleaning, EDA, testing | ||

8 | Relationships between two continuous variables | |

Measures of association for categorical variables | ||

Association versus causation – the simpson’s paradox | ||

9 | Measure of association between continuous variables | |

Simple linear regression | ||

Type I error and power: exploring the performance of the t-test on normal and non-normal data | ||

10 | Simple linear regression model | |

Hypotheses testing in simple linear regression | ||

The lm() function and linear models | ||

11 | Residuals, degrees of freedom, and goodness-of-fit | |

No class - Thanksgiving | ||

A case study: data cleaning, EDA, modeling |