Statistical Analysis and Data Mining

Choosing the right statistical tool and providing accurate results is not an easy task. At a PhD level, Numbers Insight delivers a state of the art statistics analysis report that is intended to be understood by any manager in your organization.

Below we give you a growing list of statistical and data mining methods we use for better business data analysis.

Statistical Analysis
    * Analysis of Variance
    * Bivariate and multivariate linear and nonlinear regression analysis
    * Categorical data analysis
    * Nonparametric analysis
    * Hierarchical or repeated measure models
    * Experimental designs

Market Research
    * Conjoint analysis
    * Correspondence analysis
    * Discrete choice modeling
    * Multidimensional preference analysis

Survey Data Analysis
    * Sample selection
    * Sample size computation
    * Variance estimation
    * Post stratification

Forecasting/Time Series Analysis
    * ARCH and GARCH models
    * Box-Jenkins ARIMA models
    * Exponential smoothing
    * Intervention models
    * Time trend models
    * Customized models

Operations Research
    * Linear, integer, and nonlinear programming
    * Quality control charts
    * Survival analysis
    * Decision analysis
    * Queueing simulation

Data Mining
    * Classification and decision trees
    * Cluster analysis
    * Neural networks
    * Outlier detection