Virtual Labs

Numerical differentiation using various methods with evenly spaced points

Numerical differentiation is mainly used for?

a: Finding values of integrals Explanation

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b: Estimating rate of change from discrete data Explanation

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c: Drawing 3D models Explanation

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d: Solving algebraic equations Explanation

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Which method uses the next point to approximate the derivative?

a: Forward Difference Explanation

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b: Backward Difference Explanation

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c: Central Difference Explanation

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d: Integration Method Explanation

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Backward Difference method is especially useful at:

a: The first point of the dataset Explanation

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b: The last point of the dataset Explanation

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c: Any point in the middle Explanation

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d: Points where function is unknown Explanation

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Central Difference method provides:

a: Lower accuracy than forward difference Explanation

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b: Higher accuracy because it uses both sides of the point Explanation

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c: Random estimation Explanation

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d: Accuracy only for first data point Explanation

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Evenly spaced points mean:

a: Data points are placed at equal distances Explanation

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b: Data values always increase Explanation

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c: Data is collected only at integer values Explanation

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d: Data points are randomly spaced Explanation

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Which difference method cannot be used at the boundaries?

a: Forward Difference Explanation

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b: Backward Difference Explanation

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c: Central Difference Explanation

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d: Both Forward and Backward Explanation

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What happens if fewer points are used in differentiation?

a: Accuracy increases Explanation

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b: Accuracy decreases Explanation

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c: No change in result Explanation

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d: Derivative becomes zero Explanation

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Which of the following is a major drawback of numerical differentiation?

a: It is too slow Explanation

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b: It amplifies noise in data Explanation

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c: It needs powerful processors Explanation

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d: It cannot be applied in engineering Explanation

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Which parameter controls the spacing between data points?

a: Start and End points Explanation

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b: Number of points Explanation

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c: Both A and B Explanation

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d: None of these Explanation

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In the simulator, the graph helps in:

a: Comparing original function and its numerical derivative Explanation

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b: Deleting function values Explanation

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c: Storing data points Explanation

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d: Encrypting mathematical operations Explanation

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