Applied Analytics – Quantitative Research Methods Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, Portfolio Optimization, Data and Decision Analytics

THIRD EDITION (2022)

The Applied CQRM Book Series showcases how the advanced analytics covered in the Certified in Quantitative Risk Management (CQRM) certification program can be applied to real-life business problems. In Volume I, we show how Risk Simulator and ROV BizStats can be used to perform quantitative analysis in graduate and postgraduate research. Pragmatic applications are emphasized in order to demystify the many elements inherent in quantitative analysis. A statistical black box will remain a black box if no one can understand the concepts despite its power and applicability. It is only when the black box methods become transparent, so that researchers can understand, apply, and convince others of their results, value-add, and applicability, that the approaches will receive widespread attention. This transparency is achieved through step-by-step applications of quantitative modeling as well as presenting multiple cases and discussing real-life applications. This book is targeted at those individuals who have completed the CQRM certification program but can also be used by anyone familiar with basic quantitative research methods–there is some-thing for everyone. It is also applicable for use as a second-year MBA/MS-level or introductory PhD textbook. The examples in the book assume some prior knowledge of the subject matter. Additional information on the CQRM program can be obtained at: www.iiper.org www.realoptionsvaluation.com

THE BASICS
Central Tendency, Spread, Skew, Kurtosis
Probability, Bayes’ Theorem, Trees, Combination, Permutation
Classical, Standard, P-Value, CI
Central Limit Theorem
Type I-IV Errors, Sampling Biases
Data Types & Collection Design

ANALYTICAL METHODS
T-Tests: Equal/Unequal/Paired Variance, F-Test, Z-Test
ANOVA, Blocked, Two-Way, ANCOVA, MANOVA
Linear/Nonlinear Correlation
Normality & Distributional Fitting: Kolmogorov-Smirnov, Chi-Square, Akaike Information Criterion, Anderson-Darling, Kuiper’s, Schwarz/Bayes, Box-Cox
Nonparametrics: Runs, Wilcoxon, Mann-Whitney, Lilliefors, Q-Q, D’Agostino-Pearson, Shapiro-Wilk-Royston, Kruskal-Wallis, Mood’s, Cochran’s Q, Friedman’s
Inter/Intra-Rater Reliability, Consistency, Diversity, Internal/External Validity, Predictability
Cohen’s Kappa, Cronbach’s Alpha, Guttman’s Lambda, Inter-Class Correlation, Kendall’s W, Shannon-Brillouin-Simpson Diversity, Homogeneity, Grubbs Outlier, Mahalanobis, Linear & Quadratic Discriminant, Hannan-Quinn, Diebold-Mariano, Pesaran-Timmermann, Precision, Error Control
Linear/Nonlinear Multivariate Regression
Multicollinearity, Heteroskedasticity
Structural Equation Modeling (SEM), Partial Least Squares (PLS)
Endogeneity, Simultaneous Equations Methods, Two-Stage Least Squares
Granger Causality, Engle-Granger
Advanced Regressions: Poisson, Deming, Ordinal Logistic, Ridge, Weighted, Bootstrap

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (DATA SCIENCE)
Bagging Linear Bootstrap
Bagging Nonlinear Bootstrap
Classification and Regression Trees CART
Custom Fit
Dimension Reduction Principal Component Analysis
Dimension Reduction Factor Analysis
Ensemble Common Fit
Ensemble Complex Fit
Ensemble Time-Series
Gaussian Mix & K-Means Segmentation
K-Nearest Neighbors
Linear Fit Model
Multivariate Discriminant Analysis (Linear)
Multivariate Discriminant Analysis (Quadratic)
Neural Network (Cosine, Tangent, Hyperbolic)
Logistic Binary Classification
Normit-Probit Binary Classification
Phylogenetic Trees & Hierarchical Clustering
Random Forest
Segmentation Clustering
Support Vector Machines SVM