Linear optimization involves nding optimal solutions

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Unformatted text preview: s and manipulation, and various types of statistical plotting. It also contains a wide range of statistical distributions. The stats package contains subpackages. Within each subpackage, the commands are grouped by functionality. > with(stats); [anova , describe , fit , importdata , random , statevalf , statplots , transform ] The stats package works with data in statistical lists , which can be standard Maple lists. A statistical list can also contain ranges and weighted values. The difference is best shown using an example. The name marks is assigned a standard list, > marks := > [64,93,75,81,45,68,72,82,76,73]; marks := [64, 93, 75, 81, 45, 68, 72, 82, 76, 73] as is readings > readings := [ 0.75, 0.75, .003, 1.01, .9125, > .04, .83, 1.01, .874, .002 ]; readings := [0.75, 0.75, 0.003, 1.01, 0.9125, 0.04, 0.83, 1.01, 0.874, 0.002] which is equivalent to the following statistical list. > readings := [ Weight(.75, 2), .003, Weight(1.01, 2), > .9125, .04, .83, .874, .002 ]; readings := [Weight(0.75, 2), 0.003, Weight(1.01, 2), 0.9125, 0.04, 0.83, 0.874, 0.002] The expression Weight(x,n ) indicates that the value x appears n times in the list. If differences less than 0.01 are so small that they are not meaningful, you can group them together, and give a range (using “..”). 4.2 The Maple Packages > readings := [ Weight(.75, 2), Weight(1.01, 2), .9125, > .04, .83, .874, Weight(0.002..0.003, 2) ]; • 99 readings := [Weight(0.75, 2), Weight(1.01, 2), 0.9125, 0.04, 0.83, 0.874, Weight(0.002..0.003, 2)] The describe subpackage contains commands for data analysis. > describe[mean](marks); 729 10 > describe[range](marks); 45..93 > describe[range](readings); 0.002..1.01 > describe[standarddeviation](readings); 0.4038750457 This package contains many statistical distributions. Generate some random data using the normal distribution, group it into ranges, and then plot a histogram of the ranges. > random_data:=[random[normald](50)]; 100 • Chapter 4: Maple Organization random _data := [−0.4386378394, −1.140005385, 0.1529160443, 0.7487697029, −0.4908898750, −0.6385850228, 0.7648245898, −0.04721150696, −1.463572278, 0.4470293004, 1.342701867, 2.162605068, −0.2620109124, 0.1093403084, −0.9886372087, −0.7765483851, −0.1231141571, 0.3876183720, 1.625165927, 1.095665255, −0.2068680316, −1.283733823, 1.583279600, 0.3045008349, −0.7304597374, 0.4996033128, 0.8670709448, −0.1729739933, −0.6819890237, 0.005183053789, 0.8876933468, −0.3758638317, 1.452138520, 2.858250470, 0.6917100232, 0.6341448687, 0.6707087107, 0.5872984199, 0.03801888006, −0.1238893314, −0.01231563388, −0.7709242575, −1.599692668, 0.8181350112, 0.08547526754, 0.09467224460, −1.407989130, 0.4128440679, −0.9586605355, −0.08180943597] > ranges:=[-5..-2,-2..-1,-1..0,0..1,1..2,2..5]; ranges := [−5.. − 2, −2.. − 1, −1..0, 0..1, 1..2, 2..5] > data_list:=transform[tallyinto](random_data,ranges); data _list := [Wei...
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This note was uploaded on 08/27/2012 for the course MATH 1100 taught by Professor Nil during the Spring '12 term at National University of Singapore.

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