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3 cards from this section. Quick, good the night before a test.
Define truncation error in your own words.
Think about terms in a series that may be omitted.
What does round-off error arise from?
Consider the limitations of calculator or computer algorithms.
What is truncation error?
Think about how series approximations work.
True or False: Round-off error can become significant with larger step sizes.
Remember the relationship between size and precision.
Analyze a dataset with a large number of noisy points—how would you prioritize handling truncation vs. round-off errors?
Think about how much each error could skew your results based on the dataset.
Given a dataset where step size is considerably small, discuss how you might analyze and present your derivative calculations while accounting for errors.
Consider how confidence in results is influenced by data quality and calculation method.
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