Description
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Splines can be used to approximate a “parametric curve” , by using a spline for each of the functions separately and then plotting the resulting function vs. . Draw a script letter on a graph paper, place a few points on the letter and take down the coordinates of the points (it doesn’t have to be many points: 5 to 12 points is enough, depending on the chosen letter, if chosen wisely). Create two arrays with these data and find the spline approximations !( ) and !( ) for each of the functions ( ) and ( ) with parameter representing the array index. Plot the resulting ! vs. !. Despite only using a few points for each letter, the resulting plot should be nice and smooth. Experiment with different “end conditions”. Explain your choice.
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The first U.S. postage stamp was issued in 1885, with the cost to mail a letter set at 2 cents. In 1917, the cost was raised to 3 cents but then was returned to 2 cents in 1919. In 1932, it was upped to 3 cents again, where it remained for 26 years. Then a series of increases took place as follows: 1958 = 4 cents, 1963 = 5 cents, 1968 = 6 cents, 1971 = 8 cents, 1974 = 10 cents, 1978 = 15 cents, 1981 = 18 cents in March and 20 cents in October, 1985 = 22 cents, 1988 = 25 cents, 1991 = 29 cents, 1995 = 32 cents, 1999 = 33 cents, 2001 = 34 cents, 2002 = 37 cents, 2006 = 39 cents, 2007 = 41 cents, 2008 = 42 cents, 2009=44 cents, 2012=45 cents, 2013=46 cents, 2014=49 cents, 2015=49 cents, 2016=47 cents (if you want to consider the prices prior to 1885, you can find them on Wikipedia: https://en.wikipedia.org/wiki/History_of_United_States_postage_rates).
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Determine the Newton interpolation polynomial for these data;
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Determine the not-a-knot cubic spline for these data;
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Using both results, to answer the questions: when will it cost 50 cents to mail a letter? Currently, the cost is 47 cents. What would each of these two types of interpolation predict using the data prior to 2016?
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Take the US census data from wiki: https://en.wikipedia.org/wiki/United_States_Census.
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Find the natural cubic spline function to interpolate the data. Plot.
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Write a script that first discarded one of the data-entry from the given table at a time and then using the spline to estimate it. Draw the plot of errors for each year. What (if anything) you can conclude from these estimates?
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