Statistical Modeling Techniques

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Statistical Modeling Techniques


A statistical model is a set of assumptions concerning the generation of the observed data, and similar data from a larger population. The model represents, often in a considerably idealized form, the data-generating process. A statistical model uses industry standard statistical modelling techniques e.g. Regression models, Time Series ARIMA models, etc. to identify relationships between variables in the dataset and provides user with a set of re-usable equation that can be used to forecast the behavior of the system.

Statistical models include Curve fitting techniques i.e. linear regression curves, Logarithmic regression curves, etc., Time Series ARIMA modelling, etc.

Following are the visualization, modelling and forecasting techniques that VisualizeIT will address:

  1. Time Series Visualization or Exploratory Data Analysis (EDA)
  2. Time Series Decomposition
  3. Time Series Forecasting
  4. Time Series Regression Modelling
    1. Univariate Regression Modelling
    2. Multivariate Regression Modelling

You can access the various Statistical Modelling techniques through the Statistical Modelling menu at VisualizeIT.

Modelling Solution: VisualizeIT offers access to a bunch of Analytical Models, Statistical Models and Simulation Mcropped-visualize_it_logo__transparent_090415.pngodels. Access to all the Analytical (Mathematical) models is free. We recommend you try out the Analytical models at VisualizeIT which are free to use and drop us a note with your suggestions, input and comments. You can access the VisualizeIT website here and the VisualizeIT modelling solution here –VisualizeIT.

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