RiskQ 4.2 Helps Users Make Accurate Predictions
March 31, 2003--The new RiskQ 4.2 application package allows Mathematica users in finance,
engineering, medicine, and science to use observational data--including
time series--to develop data models, predict events, and create customized
statistical tests and then to plan accordingly.
A recent article in Harvard Business Review demonstrates how a manager
creating a budget by using only the average demand would systematically
underestimate the cost of inventory shortfalls and surpluses. The article suggests
that he should have used a more descriptive model for his data.
With RiskQ, users can turn their data into empirical distribution
functions that can be directly manipulated by Mathematica.
Because of Mathematica's
sophisticated ability to handle symbolic operations, users can incorporate
these distributions into their models as easily as they would plug in
single values. With these distribution functions, users can do hypothesis testing;
calculate statistical measures such as standard deviations, quantiles, and
variances; and run simulations.
With Monte Carlo simulations, users can obtain frequency curves resulting
from the interacting variations of distinct trials. RiskQ offers users the
option of dramatically improving on the performance of their Monte Carlo
simulations by using an efficient constrained Latin-Hypercube algorithm.
This algorithm ensures that events dependent on the tails of distributions are
adequately studied and leads to more accurate predictions of rare but
cataclysmic events--for example, in estimating the risk of complete failure
for a mission-critical system.
Technical features of RiskQ include a variety of parametric and
nonparametric statistical functions that can be used to generate statistical analysis
for means comparisons, distributional
comparisons (Kolmogorov-Smirnov type tests), multivariate regression,
correlation analysis, ANOVA/MANOVA, and homogeneity of one-way or multiway
Intimate knowledge of Mathematica is not necessary to use RiskQ although
more powerful applications of RiskQ will certainly benefit from such knowledge.
In addition to detailed descriptions of specific RiskQ functions, the
manual includes a tutorial to introduce readers to useful Mathematica procedures
such as creating symbolic functions, controlling numerical accuracy, and
conducting simultaneous operations on all elements of a list or table.
"It's time for a shift in mind-set," states Harvard Business Review.
"Rather than 'Give me a number for my report,' what [decision-makers] should be
saying is 'Give me a distribution for my simulation.'" With RiskQ 4.2 and
Mathematica, users can build their simulations--and conduct many other
types of tests--more easily than ever before.
More information about RiskQ is available.