STAT 469
Analysis of Correlated Data
Statistics
College of Physical and Mathematical Sciences
Course Description
IID regression, heterogenous variances, SARIMA models, longitudinal data, point and areally referenced spatial data.
When Taught
Winter
Grade Rule
Grade Rule 8: A, B, C, D, E, I (Standard grade rule)
Min
3
Fixed
3
Fixed
3
Fixed
0
Title
Define Functions
Learning Outcome
Define the autocovariance and autocorrelation functions
Title
Order of an ARIMA
Learning Outcome
Select the order of an ARIMA model from the sample ACF and sample PACF
Title
Define Weak Stationarity
Learning Outcome
Define weak stationarity
Title
ARMA
Learning Outcome
Define an ARMA (p, q) process
Title
Define Process
Learning Outcome
Define white noise process
Title
Fit an ARIMA
Learning Outcome
Fit an ARIMA (p, d, q) model and generate forecasts with prediction intervals in R
Title
AR and MA Model
Learning Outcome
Derive the mean, autocovariance, and autocorrelation of an AR and MA model
