APPLICATIONS OF MULTIPLE REGRESSION TO ANALYSIS OF VARIANCE AND EXPERIMENTAL DESIGNS. ANALYSIS OF MULTIPLE CLASSIFICATIONS INVOLVING FIXED, RANDOM, AND MIXED EFFECTS, INCLUDING CROSSED AND NESTED VARIABLES OF CLASSIFICATION. EMPHASIS ON COMPUTER MODEL APPLICATIONS.

PRINCIPLES OF FEEDBACK DYNAMICS, LEVELS, RATES, DELAYS. SIMULATION LANGUAGES AND THEIR APPLICATIONS IN INDUSTRIAL AND SERVICE SYSTEMS. ANALYSIS AND INTERPRETATION OF RESULTS. RECOMMENDATION AND JUSTIFICATION OF PROPOSED ALTERNATIVES.

DEVELOPMENT OF EMPIRICAL LINEAR AND NON-LINEAR MODEL BUILDING SKILLS USING A VARIETY OF TOOLS FROM MULTIVARIATE STATISTICS AND DATA MINING. DEVELOPMENT OF SKILLS TO IDENTIFY THE MODEL THAT BEST REPRESENTS THE NATURAL RELATIONSHIP BETWEEN A NUMERICAL AND/OR CATEGORICAL RESPONSE, AND A HIGH-DIMENSIONAL SET OF EXPLANATORY VARIABLES. SPECIAL ATTENTION IS GIVEN TO DATA PRE-PROCESSING, MISSING VALUE IMPUTATION, OUTLIER DETECTION, FEATURE EXTRACTION/SELECTION, AND MODEL VALIDATION. INTRODUCTION TO UNSUPERVISED LEARNING AND MODELING TECHNIQUES FOR MULTIPLE RESPONSE VARIABLES.

ADVANCED TOPICS IN STATISTICAL PROCESS CONTROL. DESIGN OF CONTROL CHARTS. EWMA CHARTS, THE SPRT AND ITS APPLICATIONS IN QUALITY ENGINEERING: CUSUM AND CONTINUOUS SAMPLING PLANS. MULTIVARIATE CONTROL CHARTS. PRINCIPLES OF QUALITY ENGINEERING AND TAGUCHI METHODS. THE LOSS AND ITS APPLICATIONS TO MULTIRESPONSE EXPERIMENTS.