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  2. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

    It is a measure used to evaluate the performance of regression or forecasting models. It is a variant of MAPE in which the mean absolute percent errors is treated as a weighted arithmetic mean. Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3]

  3. RATS (software) - Wikipedia

    en.wikipedia.org/wiki/RATS_(software)

    The following is a list of the major procedures in econometrics and time series analysis that can be implemented in RATS. All these methods can be used in order to forecast, as well as to conduct data analysis. In addition, RATS can handle cross-sectional and panel data: Linear regression, including stepwise.

  4. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal ...

  5. Demand forecasting - Wikipedia

    en.wikipedia.org/wiki/Demand_forecasting

    An example of a model for forecasting demand is M. Roodman's (1986) demand forecasting regression model for measuring the seasonality affects on a data point being measured. [11] The model was based on a linear regression model , and is used to measure linear trends based on seasonal cycles and their affects on demand i.e. the seasonal demand ...

  6. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    The first is the STAR monthly balance approach, and the conditional expectations made and regression analysis used are both tied to one month being audited. The other method is the STAR annual balance approach, which happens on a larger scale by basing the conditional expectations and regression analysis on one year being audited.

  7. Template:Least squares and regression analysis - Wikipedia

    en.wikipedia.org/wiki/Template:Least_squares_and...

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  8. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    Example of a cubic polynomial regression, which is a type of linear regression. Although polynomial regression fits a curve model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data.

  9. Ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Ordinary_least_squares

    In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...