# Lecture 17 – Bias and Variance

Presented by Fernando Perez and Ani Adhikari

Content by Fernando Perez, Ani Adhikari, Suraj Rampure

**Important:** You may want to review Lecture 3 for a refresher on random variables.

A reminder – the right column of the table below contains *Quick Checks*. These are **not** required but suggested to help you check your understanding.

Video | Quick Check | |
---|---|---|

17.1Variance of random variables. Walking through an alternate calculation of variance. Variance of a linear transformation. |
17.1 | |

17.2Deriving the variance of a sum. Understanding covariance, correlation, and independence. |
17.2 | |

17.3Variance of an i.i.d. sum. Variance of the Bernoulli and binomial distributions. |
17.3 | |

17.4Variability of the sample mean. Reviewing inferential concepts from Data 8, but with the framework of random variables. |
17.4 | |

17.5Introducing the data generating process and prediction error. Model risk. |
17.5 | |

17.6Looking at different sources of error in our model – observation variance, model variance, and bias – and discussing how to mitigate them. |
17.6 | |

17.7Decomposing model risk into the sum of observation variance, model variance, and the square of bias. |
17.7 |