Regression Forecasting Pricing, Features, Reviews & Comparison of Alternatives

Regression Forecasting

Prediction regression equation

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Regression Forecasting overview

Prediction regression provides the automatic identification of predictor variables through multiple regression analysis and advanced statistical tests. Regression results are presented in a simple and easy to understand format to quantify the relative influence of each input variable. The work flow facilitates and iterative process to test, maintain and discard variables until a prediction regression equation can be established with maximum confidence. Supplementary statistical analysis to reveal underlying data relationships include autocorrelation under the Dubin-Watson statistic and multicollinearity between individual independent variables. Standard tests include F statistic confidence intervals, adjusted R-squared, standard errors, t-test statistics and p values. The resulting prediction regression equation can subsequently applied to integrated forecasting methods or custom data for the independent variables to produce predictions and forecasts of desired period length. Built-in forecasting options for predictive analysis include linear, polynomial and exponential methodologies. Prediction regression runs in Excel for both Mac and Windows platforms.


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United States, Asia, Australia, Canada, China and 7 other markets, Europe, Germany, India, Japan, Latin America, Middle-East and Africa, United Kingdom

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Regression Forecasting screenshot: Regression predictionExcel Multiple Regression Template

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Regression Forecasting pricing

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USD 14.00

Regression Forecasting features

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Additional information for Regression Forecasting

Key features of Regression Forecasting

  • Simple and intuitive workflow
  • Input data verification and cleaning
  • Results are displayed in user friendly explanations
  • Regression statistics for each independent variable
  • Standard errors t-statistics and p-values
  • Tests significance, autocorrelation and multicollinearity
  • Regression prediction equation is used for forecasting data
  • Options to employ polynomial, exponential or linear trends
  • Prediction is based on statistical level of confidence.
  • Accommodates any type of business or scientific data
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The regression prediction application is a fast and easy to use solution for forecasting and identifying causal relationships.