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In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of ...
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.
Predictive analytics is used in actuarial science, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.
This book is your ultimate resource for Predictive Analysis. Here you will find the most up-to-date information, analysis, background and everything you need to know.
In easy to read chapters, with extensive references and links to get you to know all there is to know about Predictive Analysis right away, covering: Predictive analytics, Statistical model, Bayesian network, Boolean analysis, Classical test theory, Common-cause and special-cause, Completely randomized design, Continuum structure function, Cumulative frequency analysis, Directional statistics, Discrete choice, Doubly stochastic model, Dummy variable (statistics), Econometric model, Epitome (image processing), Errors-in-variables models, Exponential dispersion model, First-hitting-time model, Function approximation, Generalizability theory, Generalized additive model for location, scale and shape, Generalized estimating equation, Generalized linear model, Generative model, Graphical model, Hierarchical linear modeling, Independent and identically distributed random variables, Independent component analysis, Item response theory, Latent growth modeling, Latent variable, Latent variable model, Linear model, Local independence, Log-linear modeling, Marginal model, Markov chain, Mediation (statistics), Mixed logit, Mixture (probability), Mixture model, Model selection, Moderation (statistics), Multilevel model, Multinomial probit, Neighbourhood components analysis, Observational equivalence, Parametric model, Population modeling, Power law, Power transform, Predictive modelling, Proportional hazards models, Random effects model, Random walk, Rare disease assumption, Rasch model, Rasch model estimation, Regression dilution, Regression model validation, Reification (statistics), Restricted randomization, Rubin causal model, Segmented regression, Semiparametric model, Stationary subspace analysis, Statistical interference, Stochastic process, Threshold model, Time-frequency representation, Total least squares, Business intelligence, Business Intelligence Project Planning, ABC analysis, Academic Analytics, Accounting intelligence, ActiveReports, Actuate Corporation, ADAPA, Advanced 365, Altius Consulting, American Business Media, Analytic applications, ApeSoft, BIfFI, BigChampagne, BIRT Project, Blindspots analysis, Business Intelligence 2.0, Business Intelligence Competency Center, Business Intelligence portal, Business intelligence tools, Business performance management, BusinessObjects OLAP Intelligence, Cognos Reportnet, Competitive intelligence, Competitor analysis, Competitor intelligence, Context analysis, Creative competitive intelligence, Crystal Analysis, Crystal Decisions, Customer analytics, Customer attrition, Dashboard (business), Data classification (business intelligence), Data cleansing, Data Discovery and Query Builder, Data stream mining, Data warehouse, Data warehouse appliance, Data warehouse architectures, DataCleaner, DATAllegro, Business intelligence deployment...and much more
This book explains in-depth the real drivers and workings of Predictive Analysis. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of Predictive Analysis with the objectivity of experienced professionals.