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  2. Nonlinear regression - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_regression

    In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations (iterations).

  3. Non-linear least squares - Wikipedia

    en.wikipedia.org/wiki/Non-linear_least_squares

    Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n). It is used in some forms of nonlinear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations.

  4. List of statistical software - Wikipedia

    en.wikipedia.org/wiki/List_of_statistical_software

    DataGraph – visual analysis with linear and nonlinear regression; DB Lytix – 800+ in-database models; EViews – for econometric analysis; FAME (database) – a system for managing time-series databases; GAUSS – programming language for statistics; Genedata – software for integration and interpretation of experimental data in the life ...

  5. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    Nonlinear analysis is often necessary when the data is recorded from a nonlinear system. Nonlinear systems can exhibit complex dynamic effects including bifurcations, chaos, harmonics and subharmonics that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to nonlinear system identification. [133]

  6. Origin (data analysis software) - Wikipedia

    en.wikipedia.org/wiki/Origin_(data_analysis...

    Data analyses in Origin include statistics, signal processing, curve fitting and peak analysis. Origin's curve fitting is performed by a nonlinear least squares fitter which is based on the Levenberg–Marquardt algorithm. Origin imports data files in various formats such as ASCII text, Excel, NI TDM, DIADem, NetCDF, SPC, etc.

  7. 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 ...

  8. Estimation statistics - Wikipedia

    en.wikipedia.org/wiki/Estimation_statistics

    Similarly, for a regression analysis, an analyst would report the coefficient of determination (R 2) and the model equation instead of the model's p-value. However, proponents of estimation statistics warn against reporting only a few numbers. Rather, it is advised to analyze and present data using data visualization.

  9. Regression validation - Wikipedia

    en.wikipedia.org/wiki/Regression_validation

    For example, if the functional form of the model does not match the data, R 2 can be high despite a poor model fit. Anscombe's quartet consists of four example data sets with similarly high R 2 values, but data that sometimes clearly does not fit the regression line. Instead, the data sets include outliers, high-leverage points, or non-linearities.

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