Rov Press Quantitative Research Methods Using Risk Simulator and ROV BizStats Software: Applying Econometrics, Multivariate Regression, Parametric and ... Modeling & Forecasting, & Optimization

SEVENTH EDITION (2026) INTRODUCTION Research Philosophy, Ontology, Epistemology Theory, Constructs, Propositions, Logic, Attributes of a Good Theory, Theory Building Qualitative Research: Case Study, Phenomenology, Field Research, Ethnographic Research, Grounded Theory Probabilistic & Nonprobabilistic Sampling Consistency, Reliability, Validity, Biases True/Quasi-Experimental Design THE BASICS Central Tendency, Spread, Skew, Kurtosis Probability, Bayes’ Theorem, Trees, Combination, Permutation PDF, CDF, ICDF, Binomial, Hypergeometric, Poisson, Bernoulli, Discrete Uniform, Geometric, Negative Binomial, Pascal, Arcsine, Beta, Cauchy Lorentzian, Breit Wigner, Chi-Square, Cosine, Double Log, Erlang, Exponential, Extreme Value Gumbel, F Fisher Snedecor, Gamma Erlang, Laplace, Logistic, Lognormal, Normal, Parabolic, Pareto, Pearson V, Pearson VI, PERT, Power, Student’s T, Triangular, Uniform, Weibull/Rayleigh 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: Bootstrap, Cox, Deming, DiD, Logit/Probit/Tobit, Poisson, RDD, Ridge, VAR, Weighted ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (DATA SCIENCE) Bagging 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 FORECASTING AND PREDICTIVE MODELING Forecasting Techniques Time-Series Analysis Stepwise Regression Stochastic Forecasting Nonlinear Extrapolation Box-Jenkins ARIMA J-Curve, S-Curve GARCH Markov Chain GLM/MLE: Logit, Probit, Tobit Cubic Spline, Neural Network, Combinatorial Fuzzy Logic Trendlines, RMSE, MSE, MAD, MAPE, Theil’s U Outliers, Nonlinearity, Multicollinearity, Heteroskedasticity, Autocorrelation, Structural Breaks Functional Forms Forecast Intervals, OLS, Detect/Fix Autocorrelation MONTE CARLO SIMULATION Confidence Intervals, Correlations, Precision, Tornado, Sensitivity, Fitting, Percentile Fit, Bootstrapping, Distributional Analysis, Scenarios, Structural Break, Detrending, Deseasonalizing OPTIMIZATION Algorithms: Continuous & Discrete Optimization Efficient Frontier & Stochastic Optimization