{"product_id":"applied-analytics-quantitative-research-methods-applying-monte-carlo-risk-simulation-strategic-real-options-stochastic-forecasting-portfolio-opt-paperback","title":"Applied Analytics - Quantitative Research Methods: Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, Portfolio Opt - Paperback","description":"\u003cp\u003eby \u003cb\u003eJohnathan Mun\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTHIRD EDITION (2022)\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eThe 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 \u003cp\u003e\u003c\/p\u003eTHE BASICS\u003cbr\u003eCentral Tendency, Spread, Skew, Kurtosis\u003cbr\u003eProbability, Bayes' Theorem, Trees, Combination, Permutation\u003cbr\u003eClassical, Standard, P-Value, CI\u003cbr\u003eCentral Limit Theorem\u003cbr\u003eType I-IV Errors, Sampling Biases\u003cbr\u003eData Types \u0026amp; Collection Design \u003cp\u003e\u003c\/p\u003eANALYTICAL METHODS\u003cbr\u003eT-Tests: Equal\/Unequal\/Paired Variance, F-Test, Z-Test\u003cbr\u003eANOVA, Blocked, Two-Way, ANCOVA, MANOVA\u003cbr\u003eLinear\/Nonlinear Correlation\u003cbr\u003eNormality \u0026amp; Distributional Fitting: Kolmogorov-Smirnov, Chi-Square, Akaike Information Criterion, Anderson-Darling, Kuiper's, Schwarz\/Bayes, Box-Cox\u003cbr\u003eNonparametrics: Runs, Wilcoxon, Mann-Whitney, Lilliefors, Q-Q, D'Agostino-Pearson, Shapiro-Wilk-Royston, Kruskal-Wallis, Mood's, Cochran's Q, Friedman's\u003cbr\u003eInter\/Intra-Rater Reliability, Consistency, Diversity, Internal\/External Validity, Predictability\u003cbr\u003eCohen's Kappa, Cronbach's Alpha, Guttman's Lambda, Inter-Class Correlation, Kendall's W, Shannon-Brillouin-Simpson Diversity, Homogeneity, Grubbs Outlier, Mahalanobis, Linear \u0026amp; Quadratic Discriminant, Hannan-Quinn, Diebold-Mariano, Pesaran-Timmermann, Precision, Error Control\u003cbr\u003eLinear\/Nonlinear Multivariate Regression\u003cbr\u003eMulticollinearity, Heteroskedasticity\u003cbr\u003eStructural Equation Modeling (SEM), Partial Least Squares (PLS)\u003cbr\u003eEndogeneity, Simultaneous Equations Methods, Two-Stage Least Squares\u003cbr\u003eGranger Causality, Engle-Granger\u003cbr\u003eAdvanced Regressions: Poisson, Deming, Ordinal Logistic, Ridge, Weighted, Bootstrap \u003cp\u003e\u003c\/p\u003eARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (DATA SCIENCE)\u003cbr\u003eBagging Linear Bootstrap\u003cbr\u003eBagging Nonlinear Bootstrap\u003cbr\u003eClassification and Regression Trees CART\u003cbr\u003eCustom Fit\u003cbr\u003eDimension Reduction Principal Component Analysis\u003cbr\u003eDimension Reduction Factor Analysis\u003cbr\u003eEnsemble Common Fit\u003cbr\u003eEnsemble Complex Fit\u003cbr\u003eEnsemble Time-Series\u003cbr\u003eGaussian Mix \u0026amp; K-Means Segmentation\u003cbr\u003eK-Nearest Neighbors\u003cbr\u003eLinear Fit Model\u003cbr\u003eMultivariate Discriminant Analysis (Linear)\u003cbr\u003eMultivariate Discriminant Analysis (Quadratic)\u003cbr\u003eNeural Network (Cosine, Tangent, Hyperbolic)\u003cbr\u003eLogistic Binary Classification\u003cbr\u003eNormit-Probit Binary Classification\u003cbr\u003ePhylogenetic Trees \u0026amp; Hierarchical Clustering\u003cbr\u003eRandom Forest\u003cbr\u003eSegmentation Clustering\u003cbr\u003eSupport Vector Machines SVM\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 360\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.75 x 9 x 6 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 01, 2020\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42710227058751,"sku":"9781734481105","price":24.3,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/f39c44a420750dfb7ca11fbf1026c0bb.webp?v=1765054182","url":"https:\/\/dhlswag.com\/products\/applied-analytics-quantitative-research-methods-applying-monte-carlo-risk-simulation-strategic-real-options-stochastic-forecasting-portfolio-opt-paperback","provider":"BBB","version":"1.0","type":"link"}