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Statistical Modeling with SAS/STAT Cheng Lei Department of Electrical and Computer Engineering University of Victoria April 9, 2015.

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Presentation on theme: "Statistical Modeling with SAS/STAT Cheng Lei Department of Electrical and Computer Engineering University of Victoria April 9, 2015."— Presentation transcript:

1 Statistical Modeling with SAS/STAT Cheng Lei rexlei86@uvic.ca Department of Electrical and Computer Engineering University of Victoria April 9, 2015

2 Outline ❖ Overview of SAS/STAT ❖ Statistical Models ❖ Classes of Statistical Models ❖ Next step

3 SAS/STAT Overview Over 70 procedures Most of the procedures dedicated to solving problems in statistical modeling The Output Delivery System (ODS) included to draw all kinds of graphs and produce different format result reports (PDF, HTML, CSV, and etc.)

4 Statistical Models Deterministic and Stochastic Models Model-Based and Design-Based randomness Model Specification

5 Deterministic & Stochastic Models Deterministic Models The relationship between inputs and outputs are theoretically in deterministic fashion Very important theoretical tools But impractical for experimental data

6 Deterministic & Stochastic Models Stochastic Models The outputs are uncertainly and affected by some parameters Parameters are unknown constants and needed to be estimated based on the assumptions about the model and the data

7 Model-Based & Design- Based Randomness Model-Based Randomness Innate randomness Source of random variation comes from the unknown variation in the response Design-Based Randomness Induced randomness Random variation in the data is induced by random selection

8 Model Specification Model selection diagnosis discrimination

9 Classes of Statistical Models Linear & nonlinear Models Univariate & Multivariate Models Regression Models & Models with Classification Effects Fixed, Random, and Mixed Models Generalized Linear Models Latent Variable Models Bayesian Models

10 Univariate & Multivariate Models Univariate Models Each response variable is modeled separately i.e.: Mutilvariate Models Multiple response variables are modeled jointly i.e.:

11 Latent Variable Models Involve variables not directly observed in the research Make a hypothesis the factors as the potential ones Apply regression models Apply the evaluation methods to validate the model

12 Next week’s work Cluster Procedures Classifier Procedures Output Delivery System Ways to plot graphs Ways to form result reports

13 Thank You!!!


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