means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.

Similar documents
Mu Sequences/Series Solutions National Convention 2014

n 1 n 2 n 2 n n 1 ln n 2 1 n Express the number as a ratio of integers

1 Onto functions and bijections Applications to Counting

CHAPTER 4 RADICAL EXPRESSIONS

5 Short Proofs of Simplified Stirling s Approximation

Ideal multigrades with trigonometric coefficients

Exercises for Square-Congruence Modulo n ver 11

PTAS for Bin-Packing

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

Neville Robbins Mathematics Department, San Francisco State University, San Francisco, CA (Submitted August 2002-Final Revision December 2002)

Chapter 5 Properties of a Random Sample

18.413: Error Correcting Codes Lab March 2, Lecture 8

Laboratory I.10 It All Adds Up

Evaluating Polynomials

Sequences and summations

X ε ) = 0, or equivalently, lim

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010

MA 524 Homework 6 Solutions

LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX

MATH 371 Homework assignment 1 August 29, 2013

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

ρ < 1 be five real numbers. The

Multiple Choice Test. Chapter Adequacy of Models for Regression

Centroids & Moments of Inertia of Beam Sections

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Chapter 8: Statistical Analysis of Simulated Data

Introduction to Probability

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

D KL (P Q) := p i ln p i q i

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

CHAPTER 3 POSTERIOR DISTRIBUTIONS

Fibonacci Identities as Binomial Sums

STK4011 and STK9011 Autumn 2016

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

Algorithms Theory, Solution for Assignment 2

Chapter 4 Multiple Random Variables

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

Third handout: On the Gini Index

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

CHAPTER VI Statistical Analysis of Experimental Data

The k-nacci triangle and applications

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

EECE 301 Signals & Systems

Numerical Analysis Formulae Booklet

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

Bivariate Vieta-Fibonacci and Bivariate Vieta-Lucas Polynomials

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Complex Numbers Primer

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

Point Estimation: definition of estimators

Q-analogue of a Linear Transformation Preserving Log-concavity

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015

Rademacher Complexity. Examples

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d

MATH 247/Winter Notes on the adjoint and on normal operators.

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

Econometric Methods. Review of Estimation

IFYMB002 Mathematics Business Appendix C Formula Booklet

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices.

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

Lecture 07: Poles and Zeros

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two

Class 13,14 June 17, 19, 2015

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Investigating Cellular Automata

Statistics Descriptive and Inferential Statistics. Instructor: Daisuke Nagakura

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3

MA/CSSE 473 Day 27. Dynamic programming

Integral Generalized Binomial Coefficients of Multiplicative Functions

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

The z-transform. LTI System description. Prof. Siripong Potisuk

Lebesgue Measure of Generalized Cantor Set

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Parameter, Statistic and Random Samples

Maximum Likelihood Estimation

v 1 -periodic 2-exponents of SU(2 e ) and SU(2 e + 1)

ENGI 4421 Propagation of Error Page 8-01

On the Primitive Classes of K * KHALED S. FELALI Department of Mathematical Sciences, Umm Al-Qura University, Makkah Al-Mukarramah, Saudi Arabia

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

A unified matrix representation for degree reduction of Bézier curves

Chapter 9 Jordan Block Matrices

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

h-analogue of Fibonacci Numbers

Some identities involving the partial sum of q-binomial coefficients

Generalized Linear Regression with Regularization

(b) By independence, the probability that the string 1011 is received correctly is

Non-uniform Turán-type problems

Chapter 4 Multiple Random Variables

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

Non-degenerate Perturbation Theory

2. Independence and Bernoulli Trials

General Method for Calculating Chemical Equilibrium Composition

Introduction to local (nonparametric) density estimation. methods

Dimensionality Reduction and Learning

Transcription:

9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc, geometrc or ether. 4. Fd (recursve ad explct) formulas for a arthmetc sequece. 5. Fd (recursve ad explct) formulas for a geometrc sequece. 6. Use summato otato. 7. Rewrte a seres usg summato otato. 8. Fd the sum of a arthmetc seres. 9. Fd the sum of a geometrc seres (both fte ad fte). 0. Tell whether a geometrc seres s coverget or dverget ad why. Day Day DAY : SEQUENCES Let s start wth some basc deftos Sequece: Ay set of umbers a specfc order. Each elemet the set s called a. We use a to detfy whch term we are talkg about. For example, a meas the frst term, a meas the term, etc. Two Types of Sequeces: o Ifte Sequeces: follow the same patter forever. o Fte Sequeces: follow the same patter for a fxed umber of terms. Explct Formula: Gves the sequece as a fucto of the umber of terms,. Recursve Formulas: Gves the frst term ad a fucto for fdg subsequet terms based o the prevous term. Always thk of as the prevous. Example : Tell whether gve formula s explct or recursve. The, fd the ext three terms of the sequece: a) a 0 b) a 57 a a 5for 9 -

9.4 Sequeces ad Seres Pre Calculus Arthmetc Sequeces: Ay sequece whose successve terms have a commo dfferece, d. Explct Formula: a a d( ) Recursve Formula: a # a a d for Example : Gve the arthmetc sequece,, 0,... a) Fd a recursve formula. b) Fd a 4. c) Fd a explct formula. Example 3: What s the st term of the arthmetc sequece, 4, 3,? Example 4: For the sequece below, wrte a explct formula ad fd the 0 th term. You do NOT have to fd a 3 the mddle terms! a a 4for Thk about ths Are the followg sequeces arthmetc? Fd a 0 for each of the sequeces WITHOUT fdg the mddle terms.. 79, 43, 8., 4, 6, 64, 9 -

9.4 Sequeces ad Seres Pre Calculus Geometrc Sequeces: Ay sequece whose successve terms have a commo rato, r. Explct Formula: a a r Recursve Formula: a # a a r for Example 5: Gve the sequece t 4 t 6( t ) for a) Fd the frst 4 terms of the sequece. b) Wrte a explct formula for the sequece. Example 6: Wrte a recursve formula for the sequece 50, 5,.5, 6.5,... Warm Up for 9.4 day : Fd the sum of the tegers from to 00 wthout a calculator. NO CHEATING!!!! 9-3

9.4 Sequeces ad Seres Pre Calculus DAY : SERIES Seres: the sum of the terms a sequece. Summato or Sgma Notato: Shorthad otato used to wrte fd the sum of. Most ofte used wth a explct formula. x x x x... x 3 Fte Seres: Seres wth a fxed umber of terms from a fte sequece. Ifte Seres: Seres whose terms cotue deftely; terms come from a fte sequece. Example 7: Let sequece A 8,0,3,4,7, 0 a) a 3 b) a 5 c). Fd: 3 a 6 d) a Example 8: Wrte a expresso for the sum of the fte seres usg summato otato. a) 75... b) 5 5 + 45 35 +... Arthmetc Seres: The sum of the terms a fte arthmetc sequece. a If you KNOW the last term a S Notce you eed to use ths formula. Example 9: Fd the sum of the frst 3 terms the arthmetc seres: 6 + 0... Example 0: Fd the sum of the arthmetc sequece: 7, 0, 03,, 33 9-4

9.4 Sequeces ad Seres Pre Calculus Geometrc Seres: The sum of the terms a geometrc sequece. Sum of a fte geometrc seres ( a r S ) r Notce you eed to use ths formula. Example : Fd the sum of the frst terms of the geometrc sequece: 3 3 6, 3,,,... 4 Example : Fd the sum of the geometrc seres: 6 + 8 54 +... 486 Fdg the Sum of Ifte Geometrc Seres Whle we ca fd the sum of a fte geometrc or arthmetc seres, we ca oly fd a sum of a fte geometrc seres ad the oly whe a specfc codto s met. IF, THEN YOU CAN FIND THE SUM OF AN INFINITE GEOMETRIC SERIES. If the sum ca be foud, t s foud usg the formula. If a sum s possble, we say the seres. If a sum s ot possble, we say the seres. Example 3: Determe whether the seres coverges. If t coverges, gve the sum. a) j 3 j 4 b) 3 9 7... 9-5

9. The Bomal Theorem Pre Calculus 9. THE BINOMIAL THEOREM Learg Targets:. Create Pascal s Tragle to at least sx rows usg patters.. Use combatos to fd the umbers ay row of Pascal s Tragle. 3. Expad bomal expressos. 4. Fd a specfc coeffcet of a term a bomal expaso wthout expadg the etre expresso. From the Explorato 9. ( ab) 0 ( ab) ( ab) 3 ( ab) ( ab) 4 5 ( ab) ab ab 0 0 ab ab ab 0 0 ab 3ab 3ab ab 3 0 0 3 ab 4ab 6ab 4ab ab 4 0 3 3 0 4 ab 5ab 0ab 0ab 5ab ab 5 0 4 3 3 4 0 5 Pascal s Tragle If we just look at the tragular array of coeffcets the bomal expasos above, we get the frst 5 rows of the patter kow as Pascal s Tragle. Example : Fd the coeffcets the 6 th row of Pascal s Tragle. Patters of Bomal Expasos: ( a b ) The coeffcets for each term the expaso come from ether Pascal s Tragle or the Bomal Coeffcets: C r for r 0,,,3,.... The frst term s ab 0 or a. The last term s 0 ab or b. The expoets for a decrease by for each term whle the expoets for b crease by for each term. The sum of the expoets each term s. The expaso has + terms. Example : Use Pascal s Tragle to expad the expresso ( x ) 4 y. 9-6

9. The Bomal Theorem Pre Calculus Bomal Theorem: ( ab) C a Ca b C a b... C ab C b 0 or a b a a b a b b 0 r r r ( )...... Example 3: Use the Bomal Theorem to expad the expresso ( x 3 ) 5 y. Example 4: Fd the coeffcet for the term cotag fdg the etre expaso. 7 y the expaso of (5x ) 9 y wthout actually 9-7