SAMPLE VERSUS POPULATION. Population - consists of all possible measurements that can be made on a particular item or procedure.
|
|
- Frederica Reed
- 5 years ago
- Views:
Transcription
1 SAMPLE VERSUS POPULATION Populatio - cosists of all possible measuremets that ca be made o a particular item or procedure. Ofte a populatio has a ifiite umber of data elemets Geerally expese to determie good estimates for populatio mea ad variace Sample - a subset of data selected from the populatio Ca be gathered i a ecoomical fashio May or may ot be represetative of populatio PLATE -1
2 METHODS OF ANALYZING DATA I. Numerical methods a. Rage - highest value mius the lowest value i the data set b. Dispersio - differece betwee two elemets i a data set c. Measures of cetral tedecy d. Measures of variatio II. Graphical Methods Frequecy Histogram - a bar graph based o a selected class width. Also kow as a histogram. Class - a subregio of the data. Class width - rage of a sigle class PLATE -
3 METHODS OF ANALYZING DATA Measures of cetral tedecy: 1. Mea - arithmetic mea of data. Symbolized by: A. µ for populatio, true value B. y estimate of µ determied from a sample, where y i 1. Media - physical midpoit of a data set whe data arraged i umerical order. A. If data set has odd umber of elemets, the the media is the physical midpoit of the arraged data. B. If the data set has a eve umber of elemets, the the media is the average of the two elemets that are earest the physical midpoit. 3. Mode - the most frequetly occurig elemet i the data set. Data sets ca have more oe mode. PLATE -3
4 NUMERICALLY ORDERED DATA SET Mea = 3.5 Rage = = 6.0 To create 7 uiform width classes divide rage by 7. Class width = 6.0/7 = 0.86 Add to mimum data elemet to get bouds for first class. First iterval: 0.1 x < 0.96 = Secod iterval : 0.96 x < 1.8 = etc. PLATE -4
5 A CLASS FREQUENCY TABLE Class Class Class relative iterval frequecy frequecy /50 = /50 = /50 = /50 = /50 = /50 = /50 = 0.14 = 50/50 = 1 Note that sum of class relative frequecs always oe. Use class frequecy table to costruct histogram. Histogram PLATE -5
6 COMMON HISTOGRAM SHAPES (a) (b) (c) (d) (e) Thigs that histograms idicate about data: 1. a is less precise tha b. c is bimodal 3. d is skewed to the right 4. e is skewed to the left PLATE -6
7 NUMERICALLY ORDERED DATA SET Measures of Cetral Tedecy Mea = 3.5 Media = Mode = 3.8 (occurs 3 times) PLATE -7
8 DEFINITIONS True value - A quatities theoretically correct or exact value. This value is ever kow. Error, - The differece betwee a measured quatity ad its true value. = y - µ i Residual, v - The differece betwee ay measured value ad the most probable value for a data set (the mea). Degrees of freedom, redudacies - The umber of measuremets that are i excess of the umber ecessary to solve for the ukows. PLATE -8
9 MEASURES OF DATA VARIATION Variace - a value by which the precisio of a populatio is expressed. Populatio variace, i Sample variace, v S i 1 STANDARD ERROR, - Rage withi mea that 68.3% of all observatios lie. Stadard deviatio, S - is a estimate for the stadard error of a populatio. Stadard deviatio of the mea, S y S PLATE -9
10 PRECISION VERSUS ACCURACY a b c d Results a is accurate but ot precise. b is either precise or accurate. c is precise but ot accurate. errors) d is accurate ad precise. Observer s viewpoit (Never kow its accurate) (Assumed poor) (Caused by systematic (The goal i data collectio) PLATE -10
11 ALTERNATE FORMULA FOR SAMPLE VARIANCE Expadig S i 1 ( y ) 1 S 1 1 [(y y 1 ) (y y ) (y y ) ] Substitutig i 1 for y S 1 1 y 1 y y Expadig S 1 1 y 1 y 1 y y y 1 y y PLATE -11
12 ALTERNATE FORMULA FOR SAMPLE VARIANCE Recogizig that occurs times S 1 1 ( y 1 y y ) y 1 y y Or S 1 1 ( ) ( ) Factorig ad regroupig, S 1 1 ( ) 1 ( ) 1 1 ( ) 1 ( ) Multiplyig the last term by, S 1 1 ( ) PLATE -1
13 ALTERNATE FORMULA FOR SAMPLE VARIANCE Fially, recogizig that y S ( ) y 1 NOTE: This formula ca overwhelm a hadheld calculator whe the measuremets ivolve large umbers. PLATE -13
14 EXAMPLE y = 3.5 Determiatio of stadard deviatio usig residuals. # y v v # y v v # y v v # y v v = S 50 i 1 v i ±1.37 PLATE -14
15 USING ALTERNATE FORMULA ( ) = 7, SO S 7, ( 3.5 ) sec OR S = 1.88 = ±1.37" PLATE -15
( n+1 2 ) , position=(7+1)/2 =4,(median is observation #4) Median=10lb
Chapter 3 Descriptive Measures Measures of Ceter (Cetral Tedecy) These measures will tell us where is the ceter of our data or where most typical value of a data set lies Mode the value that occurs most
More informationData Analysis. Concepts and Techniques. Chapter 2. Chapter 2: Getting to Know Your Data. Data Objects and Attribute Types
Data Aalysis Cocepts ad Techiques Chapter 2 1 Chapter 2: Gettig to Kow Your Data Data Objects ad Attribute Types Basic Statistical Descriptios of Data Data Visualizatio Measurig Data Similarity ad Dissimilarity
More informationUNIT 4 Section 8 Estimating Population Parameters using Confidence Intervals
UNIT 4 Sectio 8 Estimatig Populatio Parameters usig Cofidece Itervals To make ifereces about a populatio that caot be surveyed etirely, sample statistics ca be take from a SRS of the populatio ad used
More informationOCR Statistics 1. Working with data. Section 3: Measures of spread
Notes ad Eamples OCR Statistics 1 Workig with data Sectio 3: Measures of spread Just as there are several differet measures of cetral tedec (averages), there are a variet of statistical measures of spread.
More informationENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Descriptive Statistics
ENGI 44 Probability ad Statistics Faculty of Egieerig ad Applied Sciece Problem Set Descriptive Statistics. If, i the set of values {,, 3, 4, 5, 6, 7 } a error causes the value 5 to be replaced by 50,
More informationSD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.
SD vs. SD + Oe of the most importat uses of sample statistics is to estimate the correspodig populatio parameters. The mea of a represetative sample is a good estimate of the mea of the populatio that
More informationDescribing data with graphics and numbers
Describig data with graphics ad umbers Types of Data Categorical Variables also kow as class variables, omial variables Quatitative Variables aka umerical ariables either cotiuous or discrete. Graphig
More informationEM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS
EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS I this uit of the course we ivestigate fittig a straight lie to measured (x, y) data pairs. The equatio we wat to fit
More informationDescriptive Statistics Summary Lists
Chapter 209 Descriptive Statistics Summary Lists Itroductio This procedure is used to summarize cotiuous data. Large volumes of such data may be easily summarized i statistical lists of meas, couts, stadard
More informationPolynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0
Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity
More informationAn (or ) is a sequence in which each term after the first differs from the preceding term by a fixed constant, called the.
Sectio.2 Arithmetic Sequeces ad Series -.2 Arithmetic Sequeces ad Series Arithmetic Sequeces Arithmetic Series Key Terms: arithmetic sequece (arithmetic progressio), commo differece, arithmetic series
More information9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence
_9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to
More informationNormal Distributions
Normal Distributios Stacey Hacock Look at these three differet data sets Each histogram is overlaid with a curve : A B C A) Weights (g) of ewly bor lab rat pups B) Mea aual temperatures ( F ) i A Arbor,
More informationArea As A Limit & Sigma Notation
Area As A Limit & Sigma Notatio SUGGESTED REFERENCE MATERIAL: As you work through the problems listed below, you should referece Chapter 5.4 of the recommeded textbook (or the equivalet chapter i your
More informationAnalysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis
Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems
More informationMath 3201 Notes Chapter 4: Rational Expressions & Equations
Learig Goals: See p. tet.. Equivalet Ratioal Epressios ( classes) Read Goal p. 6 tet. Math 0 Notes Chapter : Ratioal Epressios & Equatios. Defie ad give a eample of a ratioal epressio. p. 6. Defie o-permissible
More informationCapability Analysis (Variable Data)
Capability Aalysis (Variable Data) Revised: 0/0/07 Summary... Data Iput... 3 Capability Plot... 5 Aalysis Summary... 6 Aalysis Optios... 8 Capability Idices... Prefereces... 6 Tests for Normality... 7
More informationFundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le
Fudametals of Media Processig Shi'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dih Le Today's topics Noparametric Methods Parze Widow k-nearest Neighbor Estimatio Clusterig Techiques k-meas Agglomerative Hierarchical
More information. Written in factored form it is easy to see that the roots are 2, 2, i,
CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or
More informationIntermediate Statistics
Gait Learig Guides Itermediate Statistics Data processig & display, Cetral tedecy Author: Raghu M.D. STATISTICS DATA PROCESSING AND DISPLAY Statistics is the study of data or umerical facts of differet
More informationImproving Template Based Spike Detection
Improvig Template Based Spike Detectio Kirk Smith, Member - IEEE Portlad State Uiversity petra@ee.pdx.edu Abstract Template matchig algorithms like SSE, Covolutio ad Maximum Likelihood are well kow for
More informationHow do we evaluate algorithms?
F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:
More information9 x and g(x) = 4. x. Find (x) 3.6. I. Combining Functions. A. From Equations. Example: Let f(x) = and its domain. Example: Let f(x) = and g(x) = x x 4
1 3.6 I. Combiig Fuctios A. From Equatios Example: Let f(x) = 9 x ad g(x) = 4 f x. Fid (x) g ad its domai. 4 Example: Let f(x) = ad g(x) = x x 4. Fid (f-g)(x) B. From Graphs: Graphical Additio. Example:
More informationECE4050 Data Structures and Algorithms. Lecture 6: Searching
ECE4050 Data Structures ad Algorithms Lecture 6: Searchig 1 Search Give: Distict keys k 1, k 2,, k ad collectio L of records of the form (k 1, I 1 ), (k 2, I 2 ),, (k, I ) where I j is the iformatio associated
More informationThompson s Group F (p + 1) is not Minimally Almost Convex
Thompso s Group F (p + ) is ot Miimally Almost Covex Claire Wladis Thompso s Group F (p + ). A Descriptio of F (p + ) Thompso s group F (p + ) ca be defied as the group of piecewiseliear orietatio-preservig
More informationDATA MINING II - 1DL460
DATA MINING II - 1DL460 Sprig 2017 A secod course i data miig http://www.it.uu.se/edu/course/homepage/ifoutv2/vt17/ Kjell Orsbor Uppsala Database Laboratory Departmet of Iformatio Techology, Uppsala Uiversity,
More informationData Preprocessing. Motivation
Data Preprocessig Mirek Riedewald Some slides based o presetatio by Jiawei Ha ad Michelie Kamber Motivatio Garbage-i, garbage-out Caot get good miig results from bad data Need to uderstad data properties
More informationThe Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana
The Closest Lie to a Data Set i the Plae David Gurey Southeaster Louisiaa Uiversity Hammod, Louisiaa ABSTRACT This paper looks at three differet measures of distace betwee a lie ad a data set i the plae:
More informationMath Section 2.2 Polynomial Functions
Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably
More informationCSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)
CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a
More information1.2 Binomial Coefficients and Subsets
1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =
More informationAlpha Individual Solutions MAΘ National Convention 2013
Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5
More informationCh 9.3 Geometric Sequences and Series Lessons
Ch 9.3 Geometric Sequeces ad Series Lessos SKILLS OBJECTIVES Recogize a geometric sequece. Fid the geeral, th term of a geometric sequece. Evaluate a fiite geometric series. Evaluate a ifiite geometric
More informationPerformance Plus Software Parameter Definitions
Performace Plus+ Software Parameter Defiitios/ Performace Plus Software Parameter Defiitios Chapma Techical Note-TG-5 paramete.doc ev-0-03 Performace Plus+ Software Parameter Defiitios/2 Backgroud ad Defiitios
More informationCHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs
CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics
More informationITU-T G Definitions and terminology for synchronization in packet networks
I t e r a t i o a l T e l e c o m m u i c a t i o U i o ITU-T G.826 TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU (8/215) SERIES G: TRANSMISSION SYSTEMS AND MEDIA, DIGITAL SYSTEMS AND NETWORKS Packet
More informationA New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method
A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro
More informationMath 10C Long Range Plans
Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will
More informationSouth Slave Divisional Education Council. Math 10C
South Slave Divisioal Educatio Coucil Math 10C Curriculum Package February 2012 12 Strad: Measuremet Geeral Outcome: Develop spatial sese ad proportioal reasoig It is expected that studets will: 1. Solve
More informationn n B. How many subsets of C are there of cardinality n. We are selecting elements for such a
4. [10] Usig a combiatorial argumet, prove that for 1: = 0 = Let A ad B be disjoit sets of cardiality each ad C = A B. How may subsets of C are there of cardiality. We are selectig elemets for such a subset
More informationThe Magma Database file formats
The Magma Database file formats Adrew Gaylard, Bret Pikey, ad Mart-Mari Breedt Johaesburg, South Africa 15th May 2006 1 Summary Magma is a ope-source object database created by Chris Muller, of Kasas City,
More informationWhat is the difference between a statistician and a mortician? Nobody's dying to see the statistician! Chapter 8 Interval Estimation
Slides Preared by JOHN S. LOUCKS St. Edward s Uiversity Slide 1 What is the differece betwee a statisticia ad a morticia? Nobody's dyig to see the statisticia! Slide Chater 8 Iterval Estimatio Poulatio
More informationConvex hull ( 凸殻 ) property
Covex hull ( 凸殻 ) property The covex hull of a set of poits S i dimesios is the itersectio of all covex sets cotaiig S. For N poits P,..., P N, the covex hull C is the give by the expressio The covex hull
More informationBOOLEAN MATHEMATICS: GENERAL THEORY
CHAPTER 3 BOOLEAN MATHEMATICS: GENERAL THEORY 3.1 ISOMORPHIC PROPERTIES The ame Boolea Arithmetic was chose because it was discovered that literal Boolea Algebra could have a isomorphic umerical aspect.
More informationCIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8)
CIS 11 Data Structures ad Algorithms with Java Fall 017 Big-Oh Notatio Tuesday, September 5 (Make-up Friday, September 8) Learig Goals Review Big-Oh ad lear big/small omega/theta otatios Practice solvig
More informationNTH, GEOMETRIC, AND TELESCOPING TEST
NTH, GEOMETRIC, AND TELESCOPING TEST Sectio 9. Calculus BC AP/Dual, Revised 08 viet.dag@humbleisd.et /4/08 0:0 PM 9.: th, Geometric, ad Telescopig Test SUMMARY OF TESTS FOR SERIES Lookig at the first few
More informationTutorial on Packet Time Metrics
Power Matters. Tutorial o Packet Time Metrics Lee Cosart lee.cosart@microsemi.com ITS 204 204 Microsemi Corporatio. COMPANY POPIETAY Itroductio requecy trasport Oe-way: forward & reverse packet streams
More information3. b. Present a combinatorial argument that for all positive integers n : : 2 n
. b. Preset a combiatorial argumet that for all positive itegers : : Cosider two distict sets A ad B each of size. Sice they are distict, the cardiality of A B is. The umber of ways of choosig a pair of
More informationA MODIFIED APPROACH FOR ESTIMATING PROCESS CAPABILITY INDICES USING IMPROVED ESTIMATORS
Pak. J. Statist. 017 Vol. 33(), 411-418 A MODIFIED APPROACH FOR ESTIMATING PROCESS CAPABILITY INDICES USING IMPROVED ESTIMATORS Seem Şaha Vahaplar 1 ad Özlem Ege Oruç Departmet of Statistics, Dokuz Eylül
More informationChapter 3. Floating Point Arithmetic
COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 3 Floatig Poit Arithmetic Review - Multiplicatio 0 1 1 0 = 6 multiplicad 32-bit ALU shift product right multiplier add
More informationPackage popkorn. R topics documented: February 20, Type Package
Type Pacage Pacage popkor February 20, 2015 Title For iterval estimatio of mea of selected populatios Versio 0.3-0 Date 2014-07-04 Author Vi Gopal, Claudio Fuetes Maitaier Vi Gopal Depeds
More informationThe golden search method: Question 1
1. Golde Sectio Search for the Mode of a Fuctio The golde search method: Questio 1 Suppose the last pair of poits at which we have a fuctio evaluatio is x(), y(). The accordig to the method, If f(x())
More informationBezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only
Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of
More informationImage Segmentation EEE 508
Image Segmetatio Objective: to determie (etract) object boudaries. It is a process of partitioig a image ito distict regios by groupig together eighborig piels based o some predefied similarity criterio.
More informationSorting in Linear Time. Data Structures and Algorithms Andrei Bulatov
Sortig i Liear Time Data Structures ad Algorithms Adrei Bulatov Algorithms Sortig i Liear Time 7-2 Compariso Sorts The oly test that all the algorithms we have cosidered so far is compariso The oly iformatio
More informationIMP: Superposer Integrated Morphometrics Package Superposition Tool
IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College
More informationElementary Educational Computer
Chapter 5 Elemetary Educatioal Computer. Geeral structure of the Elemetary Educatioal Computer (EEC) The EEC coforms to the 5 uits structure defied by vo Neuma's model (.) All uits are preseted i a simplified
More informationCSC 220: Computer Organization Unit 11 Basic Computer Organization and Design
College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:
More informationLecture 5. Counting Sort / Radix Sort
Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018
More informationChapter 3 Descriptive Statistics Numerical Summaries
Secto 3.1 Chapter 3 Descrptve Statstcs umercal Summares Measures of Cetral Tedecy 1. Mea (Also called the Arthmetc Mea) The mea of a data set s the sum of the observatos dvded by the umber of observatos.
More information1.8 What Comes Next? What Comes Later?
35 1.8 What Comes Next? What Comes Later? A Practice Uderstadig Task For each of the followig tables, CC BY Hiroaki Maeda https://flic.kr/p/6r8odk describe how to fid the ext term i the sequece, write
More informationInformed Search. Russell and Norvig Chap. 3
Iformed Search Russell ad Norvig Chap. 3 Not all search directios are equally promisig Outlie Iformed: use problem-specific kowledge Add a sese of directio to search: work toward the goal Heuristic fuctios:
More informationMathematical Stat I: solutions of homework 1
Mathematical Stat I: solutios of homework Name: Studet Id N:. Suppose we tur over cards simultaeously from two well shuffled decks of ordiary playig cards. We say we obtai a exact match o a particular
More informationOur second algorithm. Comp 135 Machine Learning Computer Science Tufts University. Decision Trees. Decision Trees. Decision Trees.
Comp 135 Machie Learig Computer Sciece Tufts Uiversity Fall 2017 Roi Khardo Some of these slides were adapted from previous slides by Carla Brodley Our secod algorithm Let s look at a simple dataset for
More informationRecursive Procedures. How can you model the relationship between consecutive terms of a sequence?
6. Recursive Procedures I Sectio 6.1, you used fuctio otatio to write a explicit formula to determie the value of ay term i a Sometimes it is easier to calculate oe term i a sequece usig the previous terms.
More informationDesigning a learning system
CS 75 Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@cs.pitt.edu 539 Seott Square, x-5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please try
More informationName Date Hr. ALGEBRA 1-2 SPRING FINAL MULTIPLE CHOICE REVIEW #1
Name Date Hr. ALGEBRA - SPRING FINAL MULTIPLE CHOICE REVIEW #. The high temperatures for Phoeix i October of 009 are listed below. Which measure of ceter will provide the most accurate estimatio of the
More informationChapter 3 Classification of FFT Processor Algorithms
Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As
More informationThe isoperimetric problem on the hypercube
The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose
More informationOn (K t e)-saturated Graphs
Noame mauscript No. (will be iserted by the editor O (K t e-saturated Graphs Jessica Fuller Roald J. Gould the date of receipt ad acceptace should be iserted later Abstract Give a graph H, we say a graph
More informationCreating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA
Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by
More informationANN WHICH COVERS MLP AND RBF
ANN WHICH COVERS MLP AND RBF Josef Boští, Jaromír Kual Faculty of Nuclear Scieces ad Physical Egieerig, CTU i Prague Departmet of Software Egieerig Abstract Two basic types of artificial eural etwors Multi
More informationTest 4 Review. dy du 9 5. sin5 zdz. dt. 5 Ê. x 2 È 1, 3. 2cos( x) dx is less than using Simpson's. ,1 t 5 t 2. ft () t2 4.
Name: Class: Date: ID: A Test Review Short Aswer. Fid the geeral solutio of the differetial equatio below ad check the result by differetiatio. dy du 9 u. Use the error formula to estimate the error i
More information1. SWITCHING FUNDAMENTALS
. SWITCING FUNDMENTLS Switchig is the provisio of a o-demad coectio betwee two ed poits. Two distict switchig techiques are employed i commuicatio etwors-- circuit switchig ad pacet switchig. Circuit switchig
More informationSo we find a sample mean but what can we say about the General Education Statistics
So we fid a ample mea but what ca we ay about the Geeral Educatio Statitic populatio? Cla Note Cofidece Iterval for Populatio Mea (Sectio 9.) We will be doig early the ame tuff a we did i the firt ectio
More informationThe number n of subintervals times the length h of subintervals gives length of interval (b-a).
Simulator with MadMath Kit: Riema Sums (Teacher s pages) I your kit: 1. GeoGebra file: Ready-to-use projector sized simulator: RiemaSumMM.ggb 2. RiemaSumMM.pdf (this file) ad RiemaSumMMEd.pdf (educator's
More informationINTERSECTION CORDIAL LABELING OF GRAPHS
INTERSECTION CORDIAL LABELING OF GRAPHS G Meea, K Nagaraja Departmet of Mathematics, PSR Egieerig College, Sivakasi- 66 4, Virudhuagar(Dist) Tamil Nadu, INDIA meeag9@yahoocoi Departmet of Mathematics,
More informationExact Minimum Lower Bound Algorithm for Traveling Salesman Problem
Exact Miimum Lower Boud Algorithm for Travelig Salesma Problem Mohamed Eleiche GeoTiba Systems mohamed.eleiche@gmail.com Abstract The miimum-travel-cost algorithm is a dyamic programmig algorithm to compute
More informationImpact of thin film metrology on the lithographic performance of 193nm bottom antireflective coatings
Impact of thi film metrology o the lithographic performace of 193m bottom atireflective coatigs Chris A. Mack a, Dale Harriso b, Cristia Rivas b, ad Phillip Walsh b a Lithoguru.com, Austi, TX b MetroSol,
More informationChapter 2 and 3, Data Pre-processing
CSI 4352, Itroductio to Data Miig Chapter 2 ad 3, Data Pre-processig Youg-Rae Cho Associate Professor Departmet of Computer Sciece Baylor Uiversity Why Need Data Pre-processig? Icomplete Data Missig values,
More informationMAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS
Fura Uiversity Electroic Joural of Udergraduate Matheatics Volue 00, 1996 6-16 MAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS DAVID SITTON Abstract. How ay edges ca there be i a axiu atchig i a coplete
More informationArithmetic Sequences
. Arithmetic Sequeces COMMON CORE Learig Stadards HSF-IF.A. HSF-BF.A.1a HSF-BF.A. HSF-LE.A. Essetial Questio How ca you use a arithmetic sequece to describe a patter? A arithmetic sequece is a ordered
More informationOne advantage that SONAR has over any other music-sequencing product I ve worked
*gajedra* D:/Thomso_Learig_Projects/Garrigus_163132/z_productio/z_3B2_3D_files/Garrigus_163132_ch17.3d, 14/11/08/16:26:39, 16:26, page: 647 17 CAL 101 Oe advatage that SONAR has over ay other music-sequecig
More informationDescriptive Statistics: Measures of Center
Secto 2.3 Descrptve Statstcs: Measures of Ceter Frequec dstrbutos are helpful provdg formato about categorcal data, but wth umercal data we ma wat more formato. Statstc: s a umercal measure calculated
More informationAccelerating Multi Dimensional Queries in Data Warehouses
Acceleratig Multi Dimesioal Queries i Data Warehouses Russel Pears ad Brya Houlisto School of Computig ad Mathematical Scieces, Aucklad Uiversity of Techology, New Zealad Email rpears@aut.ac.z Data Warehouses
More informationAdministrative UNSUPERVISED LEARNING. Unsupervised learning. Supervised learning 11/25/13. Final project. No office hours today
Admiistrative Fial project No office hours today UNSUPERVISED LEARNING David Kauchak CS 451 Fall 2013 Supervised learig Usupervised learig label label 1 label 3 model/ predictor label 4 label 5 Supervised
More informationHash Tables. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015.
Presetatio for use with the textbook Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 2015 Hash Tables xkcd. http://xkcd.com/221/. Radom Number. Used with permissio uder Creative
More informationLower Bounds for Sorting
Liear Sortig Topics Covered: Lower Bouds for Sortig Coutig Sort Radix Sort Bucket Sort Lower Bouds for Sortig Compariso vs. o-compariso sortig Decisio tree model Worst case lower boud Compariso Sortig
More informationCOSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1
COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,
More informationOn Computing the Fuzzy Weighted Average Using the KM Algorithms
O Computig the Fuzzy Weighted Average Usig the KM Algorithms Feilog iu ad Jerry M Medel Sigal ad Image Processig Istitute, Departmet of Electrical Egieerig Uiversity of Souther Califoria, 3740 McClitock
More informationUsing The Central Limit Theorem for Belief Network Learning
Usig The Cetral Limit Theorem for Belief Network Learig Ia Davidso, Mioo Amiia Computer Sciece Dept, SUNY Albay Albay, NY, USA,. davidso@cs.albay.edu Abstract. Learig the parameters (coditioal ad margial
More informationFrequency Distributions
Frequency Distributions RAW DATA Raw data are collected data that have not been organized numerically. An example is the set of heights of 100 male students obtained from an alphabetical listing of university
More informationSection 7.2: Direction Fields and Euler s Methods
Sectio 7.: Directio ields ad Euler s Methods Practice HW from Stewart Tetbook ot to had i p. 5 # -3 9-3 odd or a give differetial equatio we wat to look at was to fid its solutio. I this chapter we will
More informationIntro to Scientific Computing: Solutions
Itro to Scietific Computig: Solutios Dr. David M. Goulet. How may steps does it take to separate 3 objects ito groups of 4? We start with 5 objects ad apply 3 steps of the algorithm to reduce the pile
More informationG r a d e. 5 M a t h e M a t i c s. shape and space
G r a d e 5 M a t h e M a t i c s shape ad space Grade 5: Shape ad Space (Measuremet) (5.SS.1) Edurig Uderstadigs: there is o direct relatioship betwee perimeter ad area. Geeral Outcome: Use direct or
More informationCSE 111 Bio: Program Design I Lecture 17: software development, list methods
CSE 111 Bio: Program Desig I Lecture 17: software developmet, list methods Robert H. Sloa(CS) & Rachel Poretsky(Bio) Uiversity of Illiois, Chicago October 19, 2017 NESTED LOOPS: REVIEW Geerate times table
More informationOptimal Mapped Mesh on the Circle
Koferece ANSYS 009 Optimal Mapped Mesh o the Circle doc. Ig. Jaroslav Štigler, Ph.D. Bro Uiversity of Techology, aculty of Mechaical gieerig, ergy Istitut, Abstract: This paper brigs out some ideas ad
More informationNumerical Methods Lecture 6 - Curve Fitting Techniques
Numerical Methods Lecture 6 - Curve Fittig Techiques Topics motivatio iterpolatio liear regressio higher order polyomial form expoetial form Curve fittig - motivatio For root fidig, we used a give fuctio
More informationCSE 417: Algorithms and Computational Complexity
Time CSE 47: Algorithms ad Computatioal Readig assigmet Read Chapter of The ALGORITHM Desig Maual Aalysis & Sortig Autum 00 Paul Beame aalysis Problem size Worst-case complexity: max # steps algorithm
More informationWhich movie we can suggest to Anne?
ECOLE CENTRALE SUPELEC MASTER DSBI DECISION MODELING TUTORIAL COLLABORATIVE FILTERING AS A MODEL OF GROUP DECISION-MAKING You kow that the low-tech way to get recommedatios for products, movies, or etertaiig
More information