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Sinopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, signal processing, and time series analysis, a sinusoidal model to approximate a sequence Yi is: Yi = C + αsin(ωTi + φ) + Ei where C is constant defining a mean level, α is an amplitude for the sine wave, ω is the frequency, Ti is a time variable, φ is the phase, and Ei is the error sequence in approximating the sequence Yi by the model. This sinusoidal model can be fit using nonlinear least squares; to obtain a good fit, nonlinear least squares routines may require good starting values for the constant, the amplitude, and the frequency. Fitting a model with a single sinusoid is a special case of least-squares spectral analysis.

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Reseña del editor

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, signal processing, and time series analysis, a sinusoidal model to approximate a sequence Yi is: Yi = C + αsin(ωTi + φ) + Ei where C is constant defining a mean level, α is an amplitude for the sine wave, ω is the frequency, Ti is a time variable, φ is the phase, and Ei is the error sequence in approximating the sequence Yi by the model. This sinusoidal model can be fit using nonlinear least squares; to obtain a good fit, nonlinear least squares routines may require good starting values for the constant, the amplitude, and the frequency. Fitting a model with a single sinusoid is a special case of least-squares spectral analysis.

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