We can help to formulate, solve, and promote dissimilar business problems in finance, marketing, retail, health care, manufacturing, transportation and other industries that require the usage of quantitative methods, such as:
- Study Design
- Survey Sampling
- Exploratory Data Analysis and Multidimensional Data Visualization
- ANOVA, MANOVA, Repeated measures, generalized mixed modeling and multilevel regression
- Regression Modeling (linear and non-linear, parametric and non-parametric, with categorical, count or continuous dependent variable, for correlated and uncorrelated data)
- Linear and non-linear regression with any types of constraints
- Robust regression
- Quantile regression
- Bayesian regression
- Structural Equation Modeling
- Latent Class Models
- Analysis of Survival Data
- Statistical Quality Control
- Multivariate Analysis (cluster analysis, factor analysis, principal component analysis, partial least square regression, etc)
- Time Series Analysis and Forecasting
- Spatial Statistics and GIS
Corresponding data can be:
- primary or/and secondary
- cross-sectional, longitudinal, time series, panel or cross-sectional time series
Combining statistical modeling with data mining, optimization, forecasting, and spatial analytics can significantly widen the list of problems, mentioned above, and lead to additional increment in ROI and other benefits.
Typical statistical project includes the following steps: 1. project assessment, 2. detailed formulation of a statistical analysis/statistical modeling problem and methodology development, 3. finding corresponding solutions, 4. deployment of statistical application within the company's decision support system.
Project assessment starts with translation of business problem into solvable counterpart and evaluation of the feasibility and success of a project implementation.
In particular, project assessment phase includes:
- evaluating business objectives and goal attainability
- checking the correspondence between objectives (business questions) and data/information availability and data sufficiency
- discussing variety of problem statements, statistical hypothesis, dependent and independent variables and selection the most appropriate ones
- discussing uncertainty in the problem parameters, data, project duration, requirements, and outcomes
- determining required resources
- discussing integration of the solution of the problem with your planning / forecasting / decision support systems and post-solution activities
- developing customized report structure
- forming multidisciplinary / cross-functional project team (if necessary)
Detailed formulation of a statistical problem
Reviewing available data/information sources in light of project objectives and business questions, collecting necessary expert judgments and creating data set for statistical analysis. Defining variables role and formulating the business problem in statistical terms. When available data is insufficient, or requirement is to develop "if-then" scenario, then an appropriate approach can be simulation. Developing adequate methodology /selecting adequate statistical method to solve the problem at hand. Calculating a justifiable sample size (utilizing power analyses).
Finding acceptable solution
Converting formulated statistical problem into solvable counterpart by applying an available commercial tool (SAS, EViews, SPSS, Latent Gold, etc). Developing the application to produce the solution, generating the solution and analyzing its properties. Interpreting and presenting results.
Deployment of the application within the company's decision support system
Assessing modelâ€™s (findings) recommendations. Creating software infrastructure to incorporate model / findings into business decision making. Setting up the report structure and reporting system. Measuring performance accuracy, identifying factors that drive discrepancy between reality and expected outcomes, and modifying/updating data/model. Embedding statistical application into company's decision support system.