Statistical Analysis

Using statistical analysis, we seek your information. For example, we can use the distribution of values for a given Examine attribute or outliers (extremely high or low values). This information is useful for relationships between different data sources, when reclassifying data or looking for data errors.
In inferential statistics, Probability Theory (based on known values) makes the probable one Predicting values or evaluating the likelihood that a pattern or trend in the data is not due to coincidences. The function provides a measure of the pattern or relationship.
Traditional methods for statistical analysis, from sample data to interpretation of results, have been used by scientists for thousands of years. However, today's data volume makes statistics more and more valuable and more meaningful than ever. Cost-effective storage, powerful computers and advanced algorithms - all this has become more common Use of computer-aided statistics.
And here we support you. No matter whether you work with large amounts of data or make permutations of your calculations - Statistical computing is essential for us statisticians today. Our most common approaches to statistical analysis include:
  • Statistical programming
    • From traditional analysis of variance and linear regression to exact methods and Statistical Visualization Techniques - Statistical programming contributes significantly to data-driven decisions in each domain.
  • Econometrics
    • Modeling, forecasting and simulating business processes for better strategic and tactical planning. This method applies statistics to the economy to predict future trends.
  • Operations research
    • Identify the actions that produce the best results, based on many possible options and results. Planning, simulation, and related modeling processes used to streamline business processes and management challenges.
  • Matrix programming
    • Powerful computer techniques for implementing your own statistical methods and exploratory data analysis with line algorithms.
  • Statistical visualization
    • Quick, interactive statistical analysis and exploration in a visual interface can be used to better understand data and create models.
  • Statistical quality improvement
    • A mathematical approach to review the quality and safety features of all aspects of production.
  • High-performance statistics
    • in-memory infrastructures and parallel processing can be more quickly adapted to predictive models Perform more modeling iterations and apply complex techniques for faster results. They are thus suitable for the biggest big data challenges.