![]() ![]() The multiple regression analysis results are displayed in user friendly explanations without requiring statistical knowledge to interpret and use.Configurable feature selection isolates the best combination of variables to best fit the regression for maximum accuracy and persistence of predictions.Categorical and logistic (binary) data variables are detected and proxy numerical equivalent mappings are used for regression analysis. The regression input data is analyzed and checked for numerical values before processing to ensure accuracy and avoid unobserved calculation errors.Common business applications for predictive analytics via multivariate regression include real estate or market valuation and sales forecasting. The generic nature of the design allows any type of data to be regressed and forecast including business and financial data, time series and scientific data. The simple and intuitive workflow allows for sound data forecasts to be developed in a timely manner. ![]()
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December 2022
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