A) The coefficient of the highest power term. B) The number of terms in the polynomial. C) The highest power of the variable in the polynomial. D) The sum of the powers of all terms in the polynomial.
A) Ignoring data outliers for better accuracy. B) Manipulating data to fit a specific pattern. C) Estimating values between known data points. D) Finding the exact values of data points.
A) Maximizing the outliers in the data. B) Minimizing the sum of squared differences between data points and the approximating function. C) Fitting the data points exactly. D) Using the median instead of the mean.
A) Cauchy's Mean Value Theorem B) Weierstrass Approximation Theorem C) Bolzano's Intermediate Value Theorem D) Rolle's Theorem
A) Interpolation is used for discrete data while approximation is for continuous data. B) Interpolation passes through all data points while approximation does not. C) Interpolation is less accurate than approximation. D) Approximation provides exact values while interpolation provides estimates.
A) They are exponential functions used for least squares approximation. B) They are rational functions used for error analysis. C) They are piecewise polynomial functions used for interpolation. D) They are trigonometric functions used for data smoothing.
A) The absence of errors in the approximation. B) The difference between the actual function and its approximation. C) The number of data points in the approximation. D) The sum of all computed errors in the approximation.
A) It increases the complexity of the approximation model. B) It introduces more noise into the data for better accuracy. C) It applies more weight to outliers in the data. D) It prevents overfitting and improves the generalization of the approximation.
A) They require fewer data points for accurate results. B) They are limited to only linear approximations. C) They can handle functions of multiple variables and interactions. D) They are less computationally intensive than univariate techniques. |