Linear Dependence and Independence

A short piece on linearly dependent and independent sets of vectors.

Autoregressive Series

We talk about autoregressive models of different orders, and introduce their mean, variance, ACF and PACF values. Its stationarity is also briefly discussed.

Introduction

We introduce some basic ideas of time series analysis and stochastic processes. Of particular importance are the concepts of stationarity and the autocovariance and sample autocovariance functions.

Matrices

Matrix algebra plays an important role in many areas of statistics, such as linear statistical models and multivariate analysis. In this chapter we introduce basic terminology and some basic matrix operations. We also introduce some basic types of matrices.

Linear Models

So far we’ve finished the main materials of this course - estimation and hypothesis testing. The starting point of all the statistical analyses …

Install Dependencies for Puppeteer on Manjaro Linux

Statistical Decision

Up till now we’ve made the assumption that the data is generated from a statistical model controlled by some parameter(s). We used estimation to …

Confidence Intervals

Confidence intervals and methods of contructing them.

Optimal Unbiased Estimator

Introducing the Minimum Variance Unbiased Estimator and the procedure of deriving it.

Sufficiency

Introducing sufficient statistics for the inference of parameters. The factorization theorem comes in handy!