IBM: Better decision Making Under Uncertain Conditions Using Monte-Carlo Simulation
Posted: 6 June 2012 | Source: IBM - Risk Analytics
IBM® SPSS® Statistics is one of the world’s leading statistical software solutions, providing predictive models and advanced analytics to help solve business and research problems. For many businesses, research institutions and statisticians, it is the de facto standard for statistical analysis.
Organizations use SPSS Statistics to:
- Understand data.
- Analyze trends.
- Forecast and plan.
- Validate assumptions.
- Drive accurate conclusions.
The SPSS Statistics environment offers a wide range of multivariate procedures for investigating complex data relationships. A number of procedures include advanced models such as general and generalized linear modeling capabilities. With general linear models, you can model relationships and interactions between many factors. The general linear model incorporates a number of different statistical models: analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), analysis of covariance (ANCOVA), repeated measures and more. General linear modeling is well suited for statisticians who analyze data with unique characteristics (for example, nested-structure data) or who describe relationships between a dependent and a set of independent variables to discover whether random effects introduce correlations between cases.
Regression models (for continuous dependent variables) are a family of classical predictive techniques, all of which involve fitting (or regressing) a line or curve to a series of observations to model effects or predict outcomes. With SPSS Statistics, you can also use regression models to predict categorical outcomes for more than two categories, easily classify data into two groups, accurately model non-linear relationships, find the best predictor from dozens of possibilities and more.