Pre-Algebra. Mix-N-Match. Line-Ups. Inside-Outside Circle. A c t. t i e s & v i
|
|
|
- Diane Burns
- 7 years ago
- Views:
Transcription
1 IV A c t Pre-Algebra v Mx-N-Match 1. Operatons on Fractons Order of Operatons Exponental Form Scentfc Notaton Area, Permeter, and Volume Greatest Common Factor or Least Common Multple Passage of Tme Place Value More Place Value Lne-Ups 1. Comparng the Values of Fractons, Decmals, and Percents Area, Permeter, and Volume Statstcs Order of Operatons Roundng t e s & 10. Propertes of Arthmetc Equvalent Ratonal Expressons Problem Solvng Wth Percent Solvng One-Step Equatons Inequaltes n One Varable Unt Prce Solvng Proportons Geometry Defntons The Countng Prncple Statstcs Greatest Common Factor or Least Common Multple Operatons on Fractons Problem Solvng wth Proportons Angle Measurement The Countng Prncple Order of Operatons Insde-Outsde Crcle 1. Area and Permeter Place Value Roundng Greatest Common Factor and Least Common Multple Order of Operatons Propertes of Arthmetc Operatons on Integers Comparng Fractons Propertes of Arthmetc Convertng Fractons, Decmals, and Percents Interpretng Data and Graphs Smplfyng Algebrac Expressons Operatons on Monomals Inequalty Graphs Percents Convertng Unts Symmetry Smple One-Step Equatons Percent Problems Cooperatve Learnng for Hgh School Mathematcs
2 V B l a c k l n e s RallyCoach 1. Factors of a Number Common Factors One-Step Equatons Operatons on Integers Equvalent Decmals, Fractons, and Percents Interpretng Graphs Problem Solvng wth Decmals, Fractons, and Percents Measurement, Surface Area, and Volume Order of Operatons Propertes of Numbers Problem Solvng wth percents, Decmals, and Fractons RoundTable 1. Operatons on Fractons Operatons on Decmals Operatons on Integers Statstcs Mx Par Rally Coach 1. Operatons on Fractons Operatons on Integers Operatons on Decmals Passage of Tme Percent of a Number Probablty Roundng Interpretng Graphs and Other Data Dsplays Probablty Percent Decrease (and Percent Increase) Surface Area and Volume of Prsms Greatest Common Factor and Least Common Multple Surface Area and Volume Cooperatve Learnng for Hgh School Mathematcs
3 VI A c t Algebra 1 v Mx-N-Match 1. Wrtng Algebrac Expressons Evaluatng Algebrac Expressons Smplfyng Algebrac Expressons Operatons on Monomals Operatons on Polynomals Lnear Equatons Factorng t e s & 8. Reducng Ratonal Expressons Addng Fractons wth Monomal Denomnators Slope-Intercept Form of a Lne Evaluatng Square Roots Operatons on Radcals Reducng Ratonal Expressons Lne-Ups 1. Evaluatng Algebrac Expresson Lnear Equatons n on Varable Quadratc Equatons Lnear Equatons n Two Varables Algebra Word Problems Solvng Systems of Equatons Evaluatng Algebrac Expressons Insde-Outsde Crcle 1. Wrtng Algebrac Expressons Operatons on Polynomals Factorng Slope of a Lne Irratonal Numbers Smplfyng Radcals Completng the Square Factorng 2nd Degree Polynomals (or Multplyng Polynomals and Monomals) Cooperatve Learnng for Hgh School Mathematcs
4 VII B l a c k l n e s RallyCoach 1. Wrtng Algebrac Expressons Evaluatng Algebrac Expressons Solvng Mult-Step Lnear Equatons Wrtng Mult-Step Equatons Operatons on Radcals Graphng Lnear Functons Algebra Word Problems n One Varable Wrtng, Evaluatng, Transformng Formulas The Real Number System Wrtng, Evaluatng, and Transformng Formulas RoundTable 1. Algebra Word Problems n on Varable Algebra Word Problems n Two Varables (Algebrac Soluton) Algebra Word Problems n Two Varables (Graphc Soluton) Operatons on Monomals s Mx Par RallyCoach 1. Operatons on Polynomals Operatons on Monomals Wrtng and Solvng Lnear Equatons Operatons on Polynomals Operatons on Ratonal Expressons (Also Known as Algebrac Fractons Wrtng the Equaton of a Lne Operatons on Radcals Solvng Quadratc Equatons Solvng Algebra Word Problems wth One Varable Solvng Algebra Word Problems wth a System of Equatons Solvng Systems of Lnear Equatons Irratonal Numbers Wrtng and Solvng Lnear Equatons Cooperatve Learnng for Hgh School Mathematcs
5 VIII A c t Geometry v Mx-N-Match 1. Geometry Defntons Angle Measures Segment Lengths Crcle Theorems t e s & 5. Coordnate Geometry Transformatonal Geometry Compostons of Transformatons Transformatonal Geometry Lne-Ups 1. Interor and Exteror Angles of a Polygon Lengths of Segments Crcle Theorems Coordnate Geometry Angle Measures Insde-Outsde Crcle 1. Geometry Defntons Begnnng Geometry Proofs Truth Value of a Gven Statement Fndng Angle Measures Congruent Trangles Propertes of Quadrlaterals Specal Rght Trangles ( Trangles and Trangles) Transformatonal Geometry 1. Begnnng Geometry Proofs (Usng Geometry Defntons) Cooperatve Learnng for Hgh School Mathematcs
6 IX B l a c k l n e s RallyCoach 1. Constructons Geometry Proofs Geometry Problem Solvng Propertes of Quadrlaterals Correspondng Parts of Congruent Trangles Constructons RoundTable 1. Constructons Geometry Proofs Crcle Problems Geometry Defntons Mx Par RallyCoach 1. Coordnate Geometry Transformatonal Geometry Geometry Defntons (wth Dagram) Quadrlaterals Smlar Rght Trangles Specal Rght Trangles More Transformatons Coordnate Geometry Cooperatve Learnng for Hgh School Mathematcs
7 X AAlgebra 2 & Trgonometry c t v Mx-N-Match 1. Equatons of Lnes Absolute Value Equatons Evaluatng Functons Inverses of Functons Doman and Range of a Functon Graphng Parabolas Fractonal and Negatve Exponents Smplfyng Ratonal Expressons wth Negatve Exponents Drect, Inverse, and Jont Varaton Powers of s t e s & 11. Operatons on Imagnary and Complex Numbers Equaton of a Crcle Evaluatng Logarthms Solvng Log Equatons Degree Measure and Radan Measure Trg Functon Values of Specal Angles Graphs of the Sne and Cosne Curves Trg Functons and Reference Angles Inverse Trg Functons Rght Trangle Trgonometry Negatve Exponents Graphs of Trgonometrc Functons Lne-Ups 1. Absolute Value Equatons/Radcal Equatons Evaluatng Functons Compostons of Functons Inverse of a Functon Wrtng the Equaton of a Lne Wrtng Quadratc Equatons Drect Varaton Undefned Fractons Repeatng Decmals Quadratc Formula Parabolas Logarthms Exponental and Logarthmc Equatons Fndng Angle Measures Usng Trgonometrc Functons Fndng Segment Lengths Usng Rght Trangle Trgonometry Reference Angles Understandng Radan Measure Radan and Degree Measure Angle Sum and Angle Dfference Formulas Solvng Trgonometrc Equatons Fndng Angle Measure Usng All Sx Trg functons Insde-Outsde Crcle 1. Rght Trangle Trgonometry Evaluatng Absolute Value Expressons Identfyng Functons Doman and Range of a Functon Ratonal Expressons Imagnary Numbers (Powers of ) Fractonal and Negatve Exponents Operatons on Complex Numbers Graphs of Quadratc Functons (Parabolas) Equatons of Crcles Evaluatng Logarthms Laws of Logarthms Cooperatve Learnng for Hgh School Mathematcs
8 XI B l a c k l n e s Insde-Outsde Crcle Cont. 13. Trg Functons n All Four Quadrants Trg Functon Values for Specal Angles Permutatons and Combnatons (Word Problems) RallyCoach 1. Coordnate Geometry Proofs Laws of Snes and Cosnes, Area of a Trangle Rght Trangle Trgonometry Functon Graphs (Compostons and Inverses) Lnear Equatons n Two Varables Solvng Systems of Equatons by Graphng Graphng Conc Sectons Graphng Lnear Equatons n three Varables Trgonometrc Graphs Measures of Central Tendency Solvng Quadratc Equatons s 1. Solvng Systems of Equatons by Graphng Statstcs Rght Trangle Trgonometry Mx Par RallyCoach 16. Graphs of Trg Functons Angle Sum and Dfference Formulas Inverse Trg Functons s 1. Trgometrc Values of Specal Angles (0, 30, 45, 60, 90, 180, 270, 360) Evaluatng Logarthms RoundTable 1. Coordnate Geometry Compostons and Inverses of Functons Trgonometrc Functons on the Non-Unt Crcle s 1. Absolute Value Equatons and Inequaltes Compostons of Functons Wrtng the Equaton of a Lne Angle Sum and Angle Dfference Formulas Graphng System of Inequaltes Transformatons on the Coordnate Plane Complex Numbers Trgonometrc Graphs Compostons of Functons and Inverses of Functons Trgonometrc Functons on the Non-Unt Crcle Graphng Trg Functons Operatons on Complex Numbers Advanced Probablty Combnatons Bnomal Expanson Equaton of a Crcle Angle Sum and Dfference Formulas Cooperatve Learnng for Hgh School Mathematcs
9 XII A c t Pre-Calculus v Mx-N-Match 1. Evaluatng Determnants Arthmetc and Geometrc Progressons Polynomal Functon Graphs Convertng Equatons Form Polar to Rectangular Form (and Vce Versa) Lne-Ups 1. Determnants Arthmetc and Geometrc Progressons Sum of an Arthmetc or Geometrc Seres Remander Theorem t e s & 5. Laws of Logarthms Factor Theorem and Remander Theorem Bnomal Expanson Venn Dagrams Matrces Dstance From a Pont to a Lne Angle of Inclnaton Summaton Arthmetc and Geometrc Progressons Insde-Outsde Crcle 1. Determnants Seres and Sequences Identfyng Conc Sectons Specal Factorng Technques Polynomal Functons and Ther Graphs Wrtng Functons Ratonal Functons Usng the Scentfc Calculator Identfyng Conc Sectons Cooperatve Learnng for Hgh School Mathematcs
10 XIII B l a c k l n e s RallyCoach 1. Graphng Interval Functons Geometrc Progressons Lnear Programmng Polar Coordnates and Demover s Theorem Lnear Programmng RoundTable 1. Solvng Systems of Equatons Usng Determnants Doman and Range of a Functon Coordnate Geometry Interval Functons Graphng Ratonal Functons Mx Par RallyCoach 1. Determnants Arthmetc Seres and Sequences Geometrc Seres Parabolas Ratonal Functon Graphs Dscrmnant and the Roots of a Quadratc Equaton Interpretng Functon Graphs Transformatons of Functons Compostons of Functons Wrtng Functons Graphng Ratonal Functons Dstance from a Pont to a Lne Lnear Functons Solvng Exponental Equatons Usng Logs Vectors Parabolas Cooperatve Learnng for Hgh School Mathematcs
8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
where the coordinates are related to those in the old frame as follows.
Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product
BERNSTEIN POLYNOMIALS
On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
v a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
Using Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
Ring structure of splines on triangulations
www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAM-Report 2014-48 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon
Loop Parallelization
- - Loop Parallelzaton C-52 Complaton steps: nested loops operatng on arrays, sequentell executon of teraton space DECLARE B[..,..+] FOR I :=.. FOR J :=.. I B[I,J] := B[I-,J]+B[I-,J-] ED FOR ED FOR analyze
PERRON FROBENIUS THEOREM
PERRON FROBENIUS THEOREM R. CLARK ROBINSON Defnton. A n n matrx M wth real entres m, s called a stochastc matrx provded () all the entres m satsfy 0 m, () each of the columns sum to one, m = for all, ()
On some special nonlevel annuities and yield rates for annuities
On some specal nonlevel annutes and yeld rates for annutes 1 Annutes wth payments n geometrc progresson 2 Annutes wth payments n Arthmetc Progresson 1 Annutes wth payments n geometrc progresson 2 Annutes
Conversion between the vector and raster data structures using Fuzzy Geographical Entities
Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,
Recurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.
Time Value of Money Module
Tme Value of Money Module O BJECTIVES After readng ths Module, you wll be able to: Understand smple nterest and compound nterest. 2 Compute and use the future value of a sngle sum. 3 Compute and use the
Least Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2016. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng
Texas Instruments 30X IIS Calculator
Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the
1. Math 210 Finite Mathematics
1. ath 210 Fnte athematcs Chapter 5.2 and 5.3 Annutes ortgages Amortzaton Professor Rchard Blecksmth Dept. of athematcal Scences Northern Illnos Unversty ath 210 Webste: http://math.nu.edu/courses/math210
7.5. Present Value of an Annuity. Investigate
7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on
Section 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
Question 2: What is the variance and standard deviation of a dataset?
Queston 2: What s the varance and standard devaton of a dataset? The varance of the data uses all of the data to compute a measure of the spread n the data. The varance may be computed for a sample of
21 Vectors: The Cross Product & Torque
21 Vectors: The Cross Product & Torque Do not use our left hand when applng ether the rght-hand rule for the cross product of two vectors dscussed n ths chapter or the rght-hand rule for somethng curl
Compiling for Parallelism & Locality. Dependence Testing in General. Algorithms for Solving the Dependence Problem. Dependence Testing
Complng for Parallelsm & Localty Dependence Testng n General Assgnments Deadlne for proect 4 extended to Dec 1 Last tme Data dependences and loops Today Fnsh data dependence analyss for loops General code
An Integrated Semantically Correct 2.5D Object Oriented TIN. Andreas Koch
An Integrated Semantcally Correct 2.5D Object Orented TIN Andreas Koch Unverstät Hannover Insttut für Photogrammetre und GeoInformaton Contents Introducton Integraton of a DTM and 2D GIS data Semantcs
Thursday, December 10, 2009 Noon - 1:50 pm Faraday 143
1. ath 210 Fnte athematcs Chapter 5.2 and 4.3 Annutes ortgages Amortzaton Professor Rchard Blecksmth Dept. of athematcal Scences Northern Illnos Unversty ath 210 Webste: http://math.nu.edu/courses/math210
Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable
Unit 1: Integers and Fractions
Unit 1: Integers and Fractions No Calculators!!! Order Pages (All in CC7 Vol. 1) 3-1 Integers & Absolute Value 191-194, 203-206, 195-198, 207-210 3-2 Add Integers 3-3 Subtract Integers 215-222 3-4 Multiply
Properties of real networks: degree distribution
Propertes of real networks: degree dstrbuton Nodes wth small degrees are most frequent. The fracton of hghly connected nodes decreases, but s not zero. Look closer: use a logarthmc plot. 10 0 10-1 10 0
SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:
SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and
Blind Estimation of Transmit Power in Wireless Networks
Bln Estmaton of Transmt Power n Wreless Networks Murtaza Zafer (IBM Research), Bongjun Ko (IBM Research), Chatschk Bskan (IBM Research) an Ivan Ho (Imperal College, UK) Transmt-power Estmaton: Problem
GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 -NORM
GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 -NORM BARRIOT Jean-Perre, SARRAILH Mchel BGI/CNES 18.av.E.Beln 31401 TOULOUSE Cedex 4 (France) Emal: jean-perre.barrot@cnes.fr 1/Introducton The
Number Sense and Operations
Number Sense and Operations representing as they: 6.N.1 6.N.2 6.N.3 6.N.4 6.N.5 6.N.6 6.N.7 6.N.8 6.N.9 6.N.10 6.N.11 6.N.12 6.N.13. 6.N.14 6.N.15 Demonstrate an understanding of positive integer exponents
POLYSA: A Polynomial Algorithm for Non-binary Constraint Satisfaction Problems with and
POLYSA: A Polynomal Algorthm for Non-bnary Constrant Satsfacton Problems wth and Mguel A. Saldo, Federco Barber Dpto. Sstemas Informátcos y Computacón Unversdad Poltécnca de Valenca, Camno de Vera s/n
Support Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
Algebra I Credit Recovery
Algebra I Credit Recovery COURSE DESCRIPTION: The purpose of this course is to allow the student to gain mastery in working with and evaluating mathematical expressions, equations, graphs, and other topics,
HIGH SCHOOL: GEOMETRY (Page 1 of 4)
HIGH SCHOOL: GEOMETRY (Page 1 of 4) Geometry is a complete college preparatory course of plane and solid geometry. It is recommended that there be a strand of algebra review woven throughout the course
Extending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set
Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits
Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.
Luby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
Lecture 2: Single Layer Perceptrons Kevin Swingler
Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCulloch-Ptts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses
Economic Interpretation of Regression. Theory and Applications
Economc Interpretaton of Regresson Theor and Applcatons Classcal and Baesan Econometrc Methods Applcaton of mathematcal statstcs to economc data for emprcal support Economc theor postulates a qualtatve
Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.
Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces
"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
) of the Cell class is created containing information about events associated with the cell. Events are added to the Cell instance
Calbraton Method Instances of the Cell class (one nstance for each FMS cell) contan ADC raw data and methods assocated wth each partcular FMS cell. The calbraton method ncludes event selecton (Class Cell
Hedging Interest-Rate Risk with Duration
FIXED-INCOME SECURITIES Chapter 5 Hedgng Interest-Rate Rsk wth Duraton Outlne Prcng and Hedgng Prcng certan cash-flows Interest rate rsk Hedgng prncples Duraton-Based Hedgng Technques Defnton of duraton
n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)
MATH 16T Exam 1 : Part I (In-Class) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total
Joe Pimbley, unpublished, 2005. Yield Curve Calculations
Joe Pmbley, unpublshed, 005. Yeld Curve Calculatons Background: Everythng s dscount factors Yeld curve calculatons nclude valuaton of forward rate agreements (FRAs), swaps, nterest rate optons, and forward
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,
A Master Time Value of Money Formula. Floyd Vest
A Master Tme Value of Money Formula Floyd Vest For Fnancal Functons on a calculator or computer, Master Tme Value of Money (TVM) Formulas are usually used for the Compound Interest Formula and for Annutes.
Higher Education Math Placement
Higher Education Math Placement Placement Assessment Problem Types 1. Whole Numbers, Fractions, and Decimals 1.1 Operations with Whole Numbers Addition with carry Subtraction with borrowing Multiplication
Algebra 1 Course Title
Algebra 1 Course Title Course- wide 1. What patterns and methods are being used? Course- wide 1. Students will be adept at solving and graphing linear and quadratic equations 2. Students will be adept
MATH 095, College Prep Mathematics: Unit Coverage Pre-algebra topics (arithmetic skills) offered through BSE (Basic Skills Education)
MATH 095, College Prep Mathematics: Unit Coverage Pre-algebra topics (arithmetic skills) offered through BSE (Basic Skills Education) Accurately add, subtract, multiply, and divide whole numbers, integers,
Thnkwell s Homeschool Precalculus Course Lesson Plan: 36 weeks
Thnkwell s Homeschool Precalculus Course Lesson Plan: 36 weeks Welcome to Thinkwell s Homeschool Precalculus! We re thrilled that you ve decided to make us part of your homeschool curriculum. This lesson
Section 5.3 Annuities, Future Value, and Sinking Funds
Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme
+ + + - - This circuit than can be reduced to a planar circuit
MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to
Chapter 7: Answers to Questions and Problems
19. Based on the nformaton contaned n Table 7-3 of the text, the food and apparel ndustres are most compettve and therefore probably represent the best match for the expertse of these managers. Chapter
Sequence of Mathematics Courses
Sequence of ematics Courses Where do I begin? Associates Degree and Non-transferable Courses (For math course below pre-algebra, see the Learning Skills section of the catalog) MATH M09 PRE-ALGEBRA 3 UNITS
MATH 0110 Developmental Math Skills Review, 1 Credit, 3 hours lab
MATH 0110 Developmental Math Skills Review, 1 Credit, 3 hours lab MATH 0110 is established to accommodate students desiring non-course based remediation in developmental mathematics. This structure will
NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582
NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 7. Root Dynamcs 7.2 Intro to Root Dynamcs We now look at the forces requred to cause moton of the root.e. dynamcs!!
Multiple stage amplifiers
Multple stage amplfers Ams: Examne a few common 2-transstor amplfers: -- Dfferental amplfers -- Cascode amplfers -- Darlngton pars -- current mrrors Introduce formal methods for exactly analysng multple
Logistic Regression. Steve Kroon
Logstc Regresson Steve Kroon Course notes sectons: 24.3-24.4 Dsclamer: these notes do not explctly ndcate whether values are vectors or scalars, but expects the reader to dscern ths from the context. Scenaro
Faraday's Law of Induction
Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy
10.2 Future Value and Present Value of an Ordinary Simple Annuity
348 Chapter 10 Annutes 10.2 Future Value and Present Value of an Ordnary Smple Annuty In compound nterest, 'n' s the number of compoundng perods durng the term. In an ordnary smple annuty, payments are
Binomial Link Functions. Lori Murray, Phil Munz
Bnomal Lnk Functons Lor Murray, Phl Munz Bnomal Lnk Functons Logt Lnk functon: ( p) p ln 1 p Probt Lnk functon: ( p) 1 ( p) Complentary Log Log functon: ( p) ln( ln(1 p)) Motvatng Example A researcher
A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression.
Lecture 3: Annuty Goals: Learn contnuous annuty and perpetuty. Study annutes whose payments form a geometrc progresson or a arthmetc progresson. Dscuss yeld rates. Introduce Amortzaton Suggested Textbook
Matrix Multiplication I
Matrx Multplcaton I Yuval Flmus February 2, 2012 These notes are based on a lecture gven at the Toronto Student Semnar on February 2, 2012. The materal s taen mostly from the boo Algebrac Complexty Theory
In our example i = r/12 =.0825/12 At the end of the first month after your payment is received your amount in the account, the balance, is
Payout annutes: Start wth P dollars, e.g., P = 100, 000. Over a 30 year perod you receve equal payments of A dollars at the end of each month. The amount of money left n the account, the balance, earns
Mathematics of Finance
5 Mathematcs of Fnance 5.1 Smple and Compound Interest 5.2 Future Value of an Annuty 5.3 Present Value of an Annuty;Amortzaton Chapter 5 Revew Extended Applcaton:Tme, Money, and Polynomals Buyng a car
A) 3.1 B) 3.3 C) 3.5 D) 3.7 E) 3.9 Solution.
ACTS 408 Instructor: Natala A. Humphreys SOLUTION TO HOMEWOR 4 Secton 7: Annutes whose payments follow a geometrc progresson. Secton 8: Annutes whose payments follow an arthmetc progresson. Problem Suppose
Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
CHAPTER 14 MORE ABOUT REGRESSION
CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp
Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College
Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure
Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3
Royal Holloway Unversty of London Department of Physs Seres Solutons of ODEs the Frobenus method Introduton to the Methodology The smple seres expanson method works for dfferental equatons whose solutons
Mean Value Coordinates for Closed Triangular Meshes
Mean Value Coordnates for Closed Trangular Meshes Tao Ju, Scott Schaefer, Joe Warren Rce Unversty (a) (b) (c) (d) Fgure : Orgnal horse model wth enclosng trangle control mesh shown n black (a). Several
How To Calculate The Accountng Perod Of Nequalty
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
Realistic Image Synthesis
Realstc Image Synthess - Combned Samplng and Path Tracng - Phlpp Slusallek Karol Myszkowsk Vncent Pegoraro Overvew: Today Combned Samplng (Multple Importance Samplng) Renderng and Measurng Equaton Random
Introduction to Statistical Physics (2SP)
Introducton to Statstcal Physcs (2SP) Rchard Sear March 5, 20 Contents What s the entropy (aka the uncertanty)? 2. One macroscopc state s the result of many many mcroscopc states.......... 2.2 States wth
Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors
Pont cloud to pont cloud rgd transformatons Russell Taylor 600.445 1 600.445 Fall 000-014 Copyrght R. H. Taylor Mnmzng Rgd Regstraton Errors Typcally, gven a set of ponts {a } n one coordnate system and
An Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
How To Assemble The Tangent Spaces Of A Manfold Nto A Coherent Whole
CHAPTER 7 VECTOR BUNDLES We next begn addressng the queston: how do we assemble the tangent spaces at varous ponts of a manfold nto a coherent whole? In order to gude the decson, consder the case of U
SIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals
FINANCIAL MATHEMATICS A Practcal Gude for Actuares and other Busness Professonals Second Edton CHRIS RUCKMAN, FSA, MAAA JOE FRANCIS, FSA, MAAA, CFA Study Notes Prepared by Kevn Shand, FSA, FCIA Assstant
Energies of Network Nastsemble
Supplementary materal: Assessng the relevance of node features for network structure Gnestra Bancon, 1 Paolo Pn,, 3 and Matteo Marsl 1 1 The Abdus Salam Internatonal Center for Theoretcal Physcs, Strada
Lecture 3: Force of Interest, Real Interest Rate, Annuity
Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and
1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
Laddered Multilevel DC/AC Inverters used in Solar Panel Energy Systems
Proceedngs of the nd Internatonal Conference on Computer Scence and Electroncs Engneerng (ICCSEE 03) Laddered Multlevel DC/AC Inverters used n Solar Panel Energy Systems Fang Ln Luo, Senor Member IEEE
KEANSBURG SCHOOL DISTRICT KEANSBURG HIGH SCHOOL Mathematics Department. HSPA 10 Curriculum. September 2007
KEANSBURG HIGH SCHOOL Mathematics Department HSPA 10 Curriculum September 2007 Written by: Karen Egan Mathematics Supervisor: Ann Gagliardi 7 days Sample and Display Data (Chapter 1 pp. 4-47) Surveys and
HÜCKEL MOLECULAR ORBITAL THEORY
1 HÜCKEL MOLECULAR ORBITAL THEORY In general, the vast maorty polyatomc molecules can be thought of as consstng of a collecton of two electron bonds between pars of atoms. So the qualtatve pcture of σ
What are the place values to the left of the decimal point and their associated powers of ten?
The verbal answers to all of the following questions should be memorized before completion of algebra. Answers that are not memorized will hinder your ability to succeed in geometry and algebra. (Everything
1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
Numerical Methods 數 值 方 法 概 說. Daniel Lee. Nov. 1, 2006
Numercal Methods 數 值 方 法 概 說 Danel Lee Nov. 1, 2006 Outlnes Lnear system : drect, teratve Nonlnear system : Newton-lke Interpolatons : polys, splnes, trg polys Approxmatons (I) : orthogonal polys Approxmatons
Schneps, Leila; Colmez, Coralie. Math on Trial : How Numbers Get Used and Abused in the Courtroom. New York, NY, USA: Basic Books, 2013. p i.
New York, NY, USA: Basic Books, 2013. p i. http://site.ebrary.com/lib/mcgill/doc?id=10665296&ppg=2 New York, NY, USA: Basic Books, 2013. p ii. http://site.ebrary.com/lib/mcgill/doc?id=10665296&ppg=3 New
Calculating the high frequency transmission line parameters of power cables
< ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,
Level Annuities with Payments Less Frequent than Each Interest Period
Level Annutes wth Payments Less Frequent than Each Interest Perod 1 Annuty-mmedate 2 Annuty-due Level Annutes wth Payments Less Frequent than Each Interest Perod 1 Annuty-mmedate 2 Annuty-due Symoblc approach
MATH. ALGEBRA I HONORS 9 th Grade 12003200 ALGEBRA I HONORS
* Students who scored a Level 3 or above on the Florida Assessment Test Math Florida Standards (FSA-MAFS) are strongly encouraged to make Advanced Placement and/or dual enrollment courses their first choices
Precalculus REVERSE CORRELATION. Content Expectations for. Precalculus. Michigan CONTENT EXPECTATIONS FOR PRECALCULUS CHAPTER/LESSON TITLES
Content Expectations for Precalculus Michigan Precalculus 2011 REVERSE CORRELATION CHAPTER/LESSON TITLES Chapter 0 Preparing for Precalculus 0-1 Sets There are no state-mandated Precalculus 0-2 Operations
Lecture 5,6 Linear Methods for Classification. Summary
Lecture 5,6 Lnear Methods for Classfcaton Rce ELEC 697 Farnaz Koushanfar Fall 2006 Summary Bayes Classfers Lnear Classfers Lnear regresson of an ndcator matrx Lnear dscrmnant analyss (LDA) Logstc regresson