LearningGuide

# Linear optimization involves nding optimal solutions

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

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...
View Full Document

## 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.

Ask a homework question - tutors are online