Math 167 Review for Test 4 Chapters 7, 8 & 9
|
|
- Loraine Kelly
- 5 years ago
- Views:
Transcription
1 Math 167 Review for Tet 4 Chapter 7, 8 & 9 Vocabulary 1. A ordered pair (a, b) i a of a equatio i term of x ad y if the equatio become a true tatemet whe a i ubtituted for x ad b i ubtituted for y. 2. The of a equatio i the et of all olutio of the equatio. 3. The of a equatio i two variable i the et of poit that correpod to all olutio of the equatio. 4. The graph of a equatio of the form y = mx + b, where m ad b are cotat i a. 5. The graph of a equatio of the form y = mx + b ha (0, b). 6. If a ad b are cotat, the the graph of y = b i a ad the graph of x = a i a. 7. Suppoe that a quatity y chage teadily from y1 to y2 a a quatity x chage teadily from x1 to x2. The the of y with repect to x i the ratio of the chage i y to the chage i x, deoted by! "#! $. % " #% $ 8. A cotat rate of chage of oe variable with repect to aother implie a betwee the variable. 9. Aume (x1, y1) ad (x2, y2) are two ditict poit of a o-vertical lie. The of the lie i the rate of chage of y with repect to x. I ymbol: m =! "#! $ = )*+, % " #% $ ) A icreaig lie ha lope, a decreaig lie ha lope, a horizotal lie ha a lope equal to, ad a vertical lie ha lope. 11. The of a liear equatio i y = mx + b. 12. The of a relatio i the et of all value of the explaatory variable. 13. The of the relatio i the et of all value of the repoe variable. 14. Each member of the domai i a, ad each member of the rage i a. 15. A i a relatio i which each iput lead to exactly oe output. 16. A relatio i a fuctio if ad oly if each vertical lie iterect the graph of the relatio at o more tha oe poit. We call thi requiremet the. 17. A i a relatio whoe equatio ca be put ito the form y = mx + b where m ad b are cotat. 18. The repoe variable of a fuctio f ca be repreeted by the expreio formed by writig the explaatory variable ame withi the parethee of f( ). We call thi repreetatio. 19. For a data poit (x, y), the i y ad the (writte y0) i the value obtaied by uig a model to predict y. 20. For a give data poit (x, y), the i the differece of the oberved value of y ad the predicted value of y. (Oberved value of y - Predicted value of y = y y0 ) 21. Suppoe ome data poit are modeled by a lie. A data poit o the lie ha. A data poit above the lie ha. A data poit below the lie ha. 22. We meaure how well a lie fit ome data poit by calculatig the. 23. For a group of data poit, the i the liear fuctio with the leat um of quared reidual. It graph i called the ad it equatio i called the. 24. The i the liear regreio fuctio for a group of data poit. 25. A i a graph that compare data value of the explaatory variable with the data poit reidual.
2 26. If the lope of a regreio lie i greatly affected by the removal of a data poit, we ay the data poit i a. 27. ted to be ifluetial poit whe they are horizotally far from the other data poit. 28. The i the proportio of the variatio i the repoe variable that i explaied by the regreio lie. 29. A i a equatio that cotai two or more variable. 30. For a umber c, if a < b, the ac < bc. 31. For a umber c, if a < b, the ac > bc. 32. A i a iequality that ca be put ito a form mx + b < 0 where m ad b are cotat ad m ¹ 0. Exercie 1. Fid the y-itercept ad graph the equatio by had for y = 4x Worldwide ale of iphoe are how i the table below for the lat three moth of variou year. Year Worldwide Sale (millio) Let be worldwide iphoe ale (i millio) for the lat three moth of the year that i t year ice a. Idetify the explaatory ad repoe variable. b. Cotruct a catterplot by had. c. Graph the model = 11.6t 37.6 by had o the catterplot. Doe the lie come cloe to the data poit? d. Ue the model to etimate worldwide ale of iphoe for the lat three moth of Did you perform iterpolatio or extrapolatio? e. Compute the error i the etimatio you made i part (d). f. Ue the model to etimate worldwide ale of iphoe for the lat three moth of Did you perform iterpolatio or extrapolatio? 3. For the 60 player picked i the 2014 draft for NBA baketball, let h be the height (i iche) of a player ad let w be the weight (i poud) of a player. For height betwee 72 ad 87 iche, icluive, a reaoable model i w = 6.54h a. What i the lope? What doe it mea i thi ituatio? b. What i the w-itercept? What doe it mea i thi ituatio? c. Graph the model by had. d. Predict the weight of draft-pick Shabazz Napier, who i 6 feet tall. 4. For fall emeter 2014, part-time tudet at Ceteary College paid $575 per credit for tuitio ad paid a madatory part-time tudet fee of $15 per emeter (Source: Ceteary College). Let T be the total cot (i dollar) of tuitio ad the fee whe takig c credit of coure.
3 a. Idetify the explaatory ad repoe variable. b. Fid the lope of a liear model. What doe it mea i thi ituatio? c. Fid a equatio of the model. d. Graph the model by had. e. What wa the total oe-emeter cot of tuitio plu part-time tudet fee for 9 credit of clae? 3x For f ( x) = -2x + 5; g( x) = ; h( x) = -2x 2 + 3x, fid the followig. 4 x + 1 a. f (-4) b. f (3) c. h(2) d. h (-1) e. g (1) f. g(-2) 6. a. Fid f (-2). b. Fid f (4). c. Fid x whe f ( x) = 0. d. Fid x whe f ( x) = -1. e. Fid the domai of f. f. Fid the rage of f. 7. a. Fid f (2). b. Fid f (-4). c. Fid x whe f ( x) = 4. d. Fid x whe f ( x) = 3. e. Fid x whe f ( x) = 0. f. Fid the domai of f. g. Fid the rage of f. 8. Let be the umber of drive-i movie ite i the Uited State at t year ice The fuctio = 4.9t model the ituatio well for the period a. Rewrite the equatio = -4.9t uig the fuctio ame f. b. Fid f(3). What doe it mea i thi ituatio? c. Fid f(0). What doe it mea i thi ituatio?
4 9. The mea umber of viewer of Fox prime-time TV how wa 9.1 millio viewer i 2010 ad decreaed by about 0.8 millio viewer util 2014 (Source: Niele). Let f(t) be the mea umber (i millio) of Fox prime-time viewer at t year ice a. Fid a equatio of f. b. Fid f(3). What doe it mea i thi ituatio? c. Etimate the percetage of America who were Fox prime-time viewer i The U.S. populatio wa millio i that year. 10. Fid a equatio of the lie that ha m ad cotai (5,4). Write the equatio i lopeitercept A form. 11. Fid a equatio of the lie that cotai the two give poit. Write the equatio i lopeitercept form. Roud the lope ad the cotat term to two decimal place i eeded. a. (-5, 4) ad ( 2, 10) b. (4.5, 2.2) ad (1.2, 7.5) 12. Let E be the erollmet (i thouad of tudet) at a college t year after the college ope. Some pair of value of t ad E are lited i the table below. Age of College Erollmet (year) (thouad of tudet) t E a. Cotruct a catterplot. b. Decribe the four characteritic of the aociatio. c. Fid a equatio that decribe the aociatio betwee t ad E. d. Graph the equatio you foud i part (c) o the catterplot. e. Fid the E-itercept. What doe it mea i thi ituatio? f. What i the lope? What doe it mea i thi ituatio? 13. The price of ki retal package from Gold Medal Sport Ò are how i the table below for variou umber of day. Let p(x) be the price (i dollar) of a ki retal package for day. Number of Day Price of Package (dollar) a. Cotruct a catterplot. b. Decribe the four characteritic of the aociatio. Compute ad iterpret r a part of your aalyi. c. Graph p() = o your catterplot.
5 d. Fid p(8). What doe it mea i thi ituatio? e. Fid whe p() = 130. What doe it mea i thi ituatio? 14. A racquetball i dropped from variou height, ad the bouce height i recorded each time. Let f(x).be the bouce height (i iche) of the racquetball after it i dropped from a iitial height of x iche. Drop Height (iche) Source: J. Lehma Bouce Height (iche) a. Cotruct a catterplot. b. Fid the liear regreio equatio for f. Doe the graph of f come cloe to the data poit? c. Fid the um of quared reidual for the regreio lie. d. Fid f(18). What doe it mea i thi ituatio? e. Fid the reidual for the predictio you made i part (c). What doe it mea i thi ituatio? f. Fid x whe f(x) = 30. What doe it mea i thi ituatio? 15. Solve the formula for the pecified variable. a. x= µ + z (Solve for z) b. (Solve for ) c. (Solve for ) x = y- y1 = m( x-x1) x1 16. The price of a adult oe-day ticket to Walt Diey World wa $46 i 2000, ad it icreaed by about $3.75 per year util 2012 (Source: The Walt Diey Compay). Let p be the price (i dollar) of a ticket at t year ice a. Fid a equatio of a liear model to decribe the ituatio. b. Solve the equatio foud i part (a) for t. c. Ue the equatio foud i part (b) to etimate i which year the price of ticket were $70, $75, $80, $85, ad $ Subtitute the give value for the variable i the compoud iequality. a. x- t < µ < x+ t ; x = 26.9, t = 2.528, = 4.9, = 20 pˆ(1 - pˆ) pˆ(1 - pˆ) b. pˆ - z < p< pˆ+ z ; pˆ = 0.45, z= 1.645, = Solve the iequality. Decribe the olutio et a a iequality, i iterval otatio, ad o a graph. 1 2 a x b x <
Arithmetic 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 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 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 informationParabolic Path to a Best Best-Fit Line:
Studet Activity : Fidig the Least Squares Regressio Lie By Explorig the Relatioship betwee Slope ad Residuals Objective: How does oe determie a best best-fit lie for a set of data? Eyeballig it may be
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 information( 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 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 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 information2) Give an example of a polynomial function of degree 4 with leading coefficient of -6
Math 165 Read ahead some cocepts from sectios 4.1 Read the book or the power poit presetatios for this sectio to complete pages 1 ad 2 Please, do ot complete the other pages of the hadout If you wat to
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 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 information1. The lines intersect. There is one solution, the point where they intersect. The system is called a consistent system.
Commo Core Math 3 Notes Uit Day Systems I. Systems of Liear Equatios A system of two liear equatios i two variables is two equatios cosidered together. To solve a system is to fid all the ordered pairs
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 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 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 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 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 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 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 informationPLEASURE TEST SERIES (XI) - 04 By O.P. Gupta (For stuffs on Math, click at theopgupta.com)
wwwtheopguptacom wwwimathematiciacom For all the Math-Gya Buy books by OP Gupta A Compilatio By : OP Gupta (WhatsApp @ +9-9650 350 0) For more stuffs o Maths, please visit : wwwtheopguptacom Time Allowed
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 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 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 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 informationMathematics Scope & Sequence Geometry
Mathematic Scope & Sequece 2018-19 Geometry Revied: Augut 2018 Firt Gradig Period (42 ) Readie Stadard() G.5A ivetigate patter to make cojecture about geometric relatiohip, icludig agle formed by parallel
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 information12-5A. Equivalent Fractions and Decimals. 1 Daily Common Core Review. Common Core. Lesson. Lesson Overview. Math Background
Lesso -A Equivalet Fractios ad Decimals Commo Core Lesso Overview Domai Number ad Operatios Fractios Cluster Uderstad decimal otatio for fractios, ad compare decimal fractios. Stadards.NF. Use decimal
More informationWebAssign Lesson 6-1b Geometric Series (Homework)
WebAssig Lesso 6-b Geometric Series (Homework) Curret Score : / 49 Due : Wedesday, July 30 204 :0 AM MDT Jaimos Skriletz Math 75, sectio 3, Summer 2 204 Istructor: Jaimos Skriletz. /2 poitsrogac alcet2
More informationPattern Recognition Systems Lab 1 Least Mean Squares
Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig
More informationCS : Programming for Non-Majors, Summer 2007 Programming Project #3: Two Little Calculations Due by 12:00pm (noon) Wednesday June
CS 1313 010: Programmig for No-Majors, Summer 2007 Programmig Project #3: Two Little Calculatios Due by 12:00pm (oo) Wedesday Jue 27 2007 This third assigmet will give you experiece writig programs that
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 informationName Date Hr. ALGEBRA 1-2 SPRING FINAL MULTIPLE CHOICE REVIEW #2
Name Date Hr. ALGEBRA - SPRING FINAL MULTIPLE CHOICE REVIEW # 5. Which measure of ceter is most appropriate for the followig data set? {7, 7, 75, 77,, 9, 9, 90} Mea Media Stadard Deviatio Rage 5. The umber
More informationHomework 1 Solutions MA 522 Fall 2017
Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear
More information4.3 Modeling with Arithmetic Sequences
Name Class Date 4.3 Modelig with Arithmetic Sequeces Essetial Questio: How ca you solve real-world problems usig arithmetic sequeces? Resource Locker Explore Iterpretig Models of Arithmetic Sequeces You
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 informationEVALUATION OF TRIGONOMETRIC FUNCTIONS
EVALUATION OF TRIGONOMETRIC FUNCTIONS Whe first exposed to trigoometric fuctios i high school studets are expected to memorize the values of the trigoometric fuctios of sie cosie taget for the special
More informationConsider the following population data for the state of California. Year Population
Assigmets for Bradie Fall 2016 for Chapter 5 Assigmet sheet for Sectios 5.1, 5.3, 5.5, 5.6, 5.7, 5.8 Read Pages 341-349 Exercises for Sectio 5.1 Lagrage Iterpolatio #1, #4, #7, #13, #14 For #1 use MATLAB
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 informationAlgorithm. Counting Sort Analysis of Algorithms
Algorithm Coutig Sort Aalysis of Algorithms Assumptios: records Coutig sort Each record cotais keys ad data All keys are i the rage of 1 to k Space The usorted list is stored i A, the sorted list will
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 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 informationCMPT 125 Assignment 2 Solutions
CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address
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 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 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 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 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 informationFINITE DIFFERENCE TIME DOMAIN METHOD (FDTD)
FINIT DIFFRNC TIM DOMAIN MTOD (FDTD) The FDTD method, proposed b Yee, 1966, is aother umerical method, used widel for the solutio of M problems. It is used to solve ope-regio scatterig, radiatio, diffusio,
More informationChapter 2. C++ Basics. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 2 C++ Basics Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 2.1 Variables ad Assigmets 2.2 Iput ad Output 2.3 Data Types ad Expressios 2.4 Simple Flow of Cotrol 2.5 Program
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 informationPseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured
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 informationReplacement Paths for Pairs of Shortest Path Edges in Directed Graphs
Replacemet Path for Pair of Shortet Path Edge i irected Graph AMIT M. BHOSLE Amazo Software evelopemet Ceter 3 rd Floor, C/o E4E Lab, ivyahree Chamber Lagford Road, Bagalore - 56 25, Idia Email: bhole@c.ucb.edu
More informationAlgorithms Chapter 3 Growth of Functions
Algorithms Chapter 3 Growth of Fuctios Istructor: Chig Chi Li 林清池助理教授 chigchi.li@gmail.com Departmet of Computer Sciece ad Egieerig Natioal Taiwa Ocea Uiversity Outlie Asymptotic otatio Stadard otatios
More informationECEN620: Network Theory Broadband Circuit Design Fall 2018
ECE60: etwork Theory Broadbad Circuit Deig Fall 08 Lecture 3: Phae-Locked Loop Sytem Sam Palermo Aalog & Mixed-Sigal Ceter Texa A&M Uiverity Aoucemet & Readig/Referece HW due Sept. 0 Chapter, 3, 5, & of
More informationFuzzy C-Means Clustering of Web Users for Educational Sites
Fuzzy C-Mea Cluterig of Web Uer for Educatioal Site Pawa Ligra, Rui Ya, ad Chad Wet Departmet of Mathematic ad Computig Sciece Sait Mary' Uiverity, Halifax, Nova Scotia, Caada, B3H 3C3 Abtract. Characterizatio
More information6.854J / J Advanced Algorithms Fall 2008
MIT OpeCourseWare http://ocw.mit.edu 6.854J / 18.415J Advaced Algorithms Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.415/6.854 Advaced Algorithms
More informationChapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.
Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4
More informationA graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n))
ca see that time required to search/sort grows with size of We How do space/time eeds of program grow with iput size? iput. time: cout umber of operatios as fuctio of iput Executio size operatio Assigmet:
More informationLU Decomposition Method
SOLUTION OF SIMULTANEOUS LINEAR EQUATIONS LU Decompositio Method Jamie Traha, Autar Kaw, Kevi Marti Uiversity of South Florida Uited States of America kaw@eg.usf.edu http://umericalmethods.eg.usf.edu Itroductio
More informationFast Fourier Transform (FFT) Algorithms
Fast Fourier Trasform FFT Algorithms Relatio to the z-trasform elsewhere, ozero, z x z X x [ ] 2 ~ elsewhere,, ~ e j x X x x π j e z z X X π 2 ~ The DFS X represets evely spaced samples of the z- trasform
More informationCopyright 2016 Ramez Elmasri and Shamkant B. Navathe
Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 18 Strategies for Query Processig Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio DBMS techiques to process a query Scaer idetifies
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 informationSAMPLE VERSUS POPULATION. Population - consists of all possible measurements that can be made on a particular item or procedure.
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
More informationPython Programming: An Introduction to Computer Science
Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to
More information3D Model Retrieval Method Based on Sample Prediction
20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer
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 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 informationDesigning a learning system
CS 75 Itro to Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@pitt.edu 539 Seott Square, -5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please
More informationCS 683: Advanced Design and Analysis of Algorithms
CS 683: Advaced Desig ad Aalysis of Algorithms Lecture 6, February 1, 2008 Lecturer: Joh Hopcroft Scribes: Shaomei Wu, Etha Feldma February 7, 2008 1 Threshold for k CNF Satisfiability I the previous lecture,
More informationInvestigation Monitoring Inventory
Ivestigatio Moitorig Ivetory Name Period Date Art Smith has bee providig the prits of a egravig to FieArt Gallery. He plas to make just 2000 more prits. FieArt has already received 70 of Art s prits. The
More informationThe Platonic solids The five regular polyhedra
The Platoic solids The five regular polyhedra Ole Witt-Hase jauary 7 www.olewitthase.dk Cotets. Polygos.... Topologically cosideratios.... Euler s polyhedro theorem.... Regular ets o a sphere.... The dihedral
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 informationChapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 10 Defiig Classes Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 10.1 Structures 10.2 Classes 10.3 Abstract Data Types 10.4 Itroductio to Iheritace Copyright 2015 Pearso Educatio,
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 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 informationOn Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract
O Ifiite Groups that are Isomorphic to its Proper Ifiite Subgroup Jaymar Talledo Baliho Abstract Two groups are isomorphic if there exists a isomorphism betwee them Lagrage Theorem states that the order
More informationCONTINUI TY. JEE-Mathematics. Illustration 1 : Solution : Illustration 2 : 1. CONTINUOUS FUNCTIONS :
J-Mathematics. CONTINUOUS FUNCTIONS : CONTINUI TY A fuctio for which a small chage i the idepedet variable causes oly a small chage ad ot a sudde jump i the depedet variable are called cotiuous fuctios.
More informationChapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 5 Fuctios for All Subtasks Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 5.1 void Fuctios 5.2 Call-By-Referece Parameters 5.3 Usig Procedural Abstractio 5.4 Testig ad Debuggig
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 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 informationThe Graphs of Polynomial Functions
Sectio 4.3 The Graphs of Polyomial Fuctios Objective 1: Uderstadig the Defiitio of a Polyomial Fuctio Defiitio Polyomial Fuctio 1 2 The fuctio ax a 1x a 2x a1x a0 is a polyomial fuctio of degree where
More information27 Refraction, Dispersion, Internal Reflection
Chapter 7 Refractio, Dispersio, Iteral Reflectio 7 Refractio, Dispersio, Iteral Reflectio Whe we talked about thi film iterferece, we said that whe light ecouters a smooth iterface betwee two trasparet
More informationIt just came to me that I 8.2 GRAPHS AND CONVERGENCE
44 Chapter 8 Discrete Mathematics: Fuctios o the Set of Natural Numbers (a) Take several odd, positive itegers for a ad write out eough terms of the 3N sequece to reach a repeatig loop (b) Show that ot
More informationWhat are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs
What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure
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 informationChapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3
More information15-859E: Advanced Algorithms CMU, Spring 2015 Lecture #2: Randomized MST and MST Verification January 14, 2015
15-859E: Advaced Algorithms CMU, Sprig 2015 Lecture #2: Radomized MST ad MST Verificatio Jauary 14, 2015 Lecturer: Aupam Gupta Scribe: Yu Zhao 1 Prelimiaries I this lecture we are talkig about two cotets:
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 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 informationOutline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis
Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis
More informationGRADIENT DESCENT. Admin 10/24/13. Assignment 5. David Kauchak CS 451 Fall 2013
Adi Assiget 5 GRADIENT DESCENT David Kauchak CS 451 Fall 2013 Math backgroud Liear odels A strog high-bias assuptio is liear separability: i 2 diesios, ca separate classes by a lie i higher diesios, eed
More informationCounting Regions in the Plane and More 1
Coutig Regios i the Plae ad More 1 by Zvezdelia Stakova Berkeley Math Circle Itermediate I Group September 016 1. Overarchig Problem Problem 1 Regios i a Circle. The vertices of a polygos are arraged o
More informationLecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming
Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis
More informationCounting II 3, 7 3, 2 3, 9 7, 2 7, 9 2, 9
Coutig II Sometimes we will wat to choose objects from a set of objects, ad we wo t be iterested i orderig them For example, if you are leavig for vacatio ad you wat to pac your suitcase with three of
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 informationMOTIF XF Extension Owner s Manual
MOTIF XF Extesio Ower s Maual Table of Cotets About MOTIF XF Extesio...2 What Extesio ca do...2 Auto settig of Audio Driver... 2 Auto settigs of Remote Device... 2 Project templates with Iput/ Output Bus
More informationLecture 18. Optimization in n dimensions
Lecture 8 Optimizatio i dimesios Itroductio We ow cosider the problem of miimizig a sigle scalar fuctio of variables, f x, where x=[ x, x,, x ]T. The D case ca be visualized as fidig the lowest poit of
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 information