Didactic unit 1: Analysis of variance and design of experiments.
T1. Analysis of variance with one factor
1.1. Introduction
1.2. Sums of squares and interpretation
1.3. Contrast of comparison of means
1.4. ANOVA table
1.5. Multiple comparisons
1.6. Validation of the hypotheses of the model
T2. Analysis of variance with several factors
2.1. Basic principles of the design of experiments
2.2. Factors and blocks
2.3. Designs with one factor and one block variable 2.4.
2.4. Designs with two factors
2.5. Other designs
Didactic unit 2: Multiple linear regression.
T3. Multiple linear regression.
3.1. Introduction
3.2. Matrix approach.
3.3. Inference on model parameters and significance of regression.
3.4. Estimation and validation of the model. Transformations
3.5. Lack of fit test
Multicollinearity and influential observations 3.7.
Methods for selecting the best set of regressors 3.8.
3.8. Exploitation of the model: prediction
Models with indicator variables
Didactic unit 3: Time series.
T4. Classical analysis and exponential smoothing of Time Series.
4.1. Classical descriptive analysis of a time series.
4.1.1. Components of a time series.
Additive and multiplicative schemes 4.2.
4.2. Exponential Smoothing Techniques
Simple Exponential Smoothing 4.2.1.
Holt's method 4.2.3.
Holt-Winters Method 4.2.3.
T5. Box-Jenkins Methodology
Basic concepts of stochastic processes 5.2.
Autocorrelation and partial autocorrelation analysis 5.3.
5.3. Autoregressive (AR), moving average (MA) and mixed (ARMA) models
5.4. ARIMA models: Identification
5.5. Seasonal models (SARIMA)
5.6. Model validation
5.7. Forecasting
4.3. Practice program
