1.
Introcution
2.
Hypothesis Testing
2.1.
Biased Estimators and Bessel's Correction
2.2.
The t-distribution
3.
Linear Regression
4.
Multiple Linear Regression
4.1.
RSS and Max Likelihood
4.2.
Matrix Chain Rule
4.3.
Bias in Terms of Multiple Linear Regression
4.4.
Deriving Multiple Linear Regression
5.
Confidence Intervals
5.1.
Test Training Splits
5.2.
Confidence Intervals
5.3.
Prediction Intervals
5.4.
Tolerance Intervals
5.5.
Displaying Dates
5.6.
Naive Forecasts
5.7.
Measuring Prediction Accuracy
5.8.
Roll Forward Validation
6.
Time Series
7.
Data Driven: Smoothing Methods
7.1.
Moving Average
7.2.
Exponential Smoothing
7.3.
State Space Models
8.
Model Based: ARIMA Models
9.
Appendix
Light
Rust
Coal
Navy
Ayu
Environmental Informatics (MATH3005)
Exponential Smoothing