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3 cards from this section. Quick, good the night before a test.
Define curve fitting in your own words.
Think about how you would explain it to a friend.
What is the purpose of minimizing errors in curve fitting?
Consider why we care about the difference between predictions and actual values.
What is curve fitting primarily used for?
Think about applications like predictions.
True or False: The least squares method can help minimize discrepancies in curve fitting.
Recall what least squares is intended to achieve.
Given a dataset of temperatures recorded over a month, write a brief outline of the steps you would take to apply curve fitting to this data. What challenges might you face?
Think about how different models might fit differently.
You have a dataset with data points that appear to follow an exponential growth pattern. Describe how you would confirm this using curve fitting.
What might you look for to confirm the growth pattern?
5 more questions in this section. Enroll free to practise them all.
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