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  2. Inertial measurement unit - Wikipedia

    en.wikipedia.org/wiki/Inertial_measurement_unit

    Complexity for these models will then be chosen according to the needed performance and the type of application considered. Ability to define this model is part of sensors and IMU manufacturers know-how. Sensors and IMU models are computed in factories through a dedicated calibration sequence using multi-axis turntables and climatic chambers.

  3. Errors-in-variables model - Wikipedia

    en.wikipedia.org/wiki/Errors-in-variables_model

    Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.

  4. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    A practical application is an experiment in which one measures current, I, and voltage, V, on a resistor in order to determine the resistance, R, using Ohm's law, R = V / I. Given the measured variables with uncertainties, I ± σ I and V ± σ V, and neglecting their possible correlation, the uncertainty in the computed quantity, σ R, is:

  5. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared estimate of errors (SSE), is the sum of the squares of residuals (deviations predicted from actual empirical values of data). It is a measure of the discrepancy between the data and an estimation model, such as a linear ...

  6. Regression dilution - Wikipedia

    en.wikipedia.org/wiki/Regression_dilution

    Carroll et al. (1995) give more detail on regression dilution in nonlinear models, presenting the regression dilution ratio methods as the simplest case of regression calibration methods, in which additional covariates may also be incorporated. [9] In general, methods for the structural model require some estimate of the variability of the x ...

  7. Surrogate model - Wikipedia

    en.wikipedia.org/wiki/Surrogate_model

    A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables.

  8. Error correction model - Wikipedia

    en.wikipedia.org/wiki/Error_correction_model

    The term error-correction relates to the fact that last-period's deviation from a long-run equilibrium, the error, influences its short-run dynamics. Thus ECMs directly estimate the speed at which a dependent variable returns to equilibrium after a change in other variables.

  9. Gauss–Markov process - Wikipedia

    en.wikipedia.org/wiki/Gauss–Markov_process

    A stationary Gauss–Markov process with variance (()) = and time constant has the following properties.. Exponential autocorrelation: () = | |.; A power spectral density (PSD) function that has the same shape as the Cauchy distribution: () = +. (Note that the Cauchy distribution and this spectrum differ by scale factors.)