Acceptance Sampling by Attributes

Size: px
Start display at page:

Download "Acceptance Sampling by Attributes"

Transcription

1 Introduction Acceptnce Smpling by Attributes Acceptnce smpling is concerned with inspection nd decision mking regrding products. Three spects of smpling re importnt: o Involves rndom smpling of n entire lot o Accept nd Reject Lots (does not chieve qulity improvement) o Lot sentencing o Audit tool Three pproches to lot sentencing: o Accept with no inspection o 100% inspection o Acceptnce smpling Resons for Acceptnce Smpling, not 100% inspection o Testing is destructive o Cost of 100% inspection is high o 100% inspection is not fesible (require too much time) o If vendor hs excellent qulity history 113

2 Advntges of Smpling o Less expensive o Reduced dmge o Reduces the mount of inspection error Disdvntges of Smpling o Risk of ccepting bd lots, rejecting good lots. o Less informtion generted o Requires plnning nd documenttion Types of Smpling Plns (ttribute smpling plns) o Single smpling pln o Double-smpling pln o Multiple-smpling pln o Sequentil-smpling Lot Formtion Considertions before inspection o Lots should be homogeneous o Lrger lots more preferble thn smller lots o Lots should be conformble to the mterils-hndling systems used in both the vendor nd consumer fcilities. 114

3 Rndom Smpling The units selected for inspection should be chosen t rndom. Rndom smples re not used, bis cn be introduced. If ny judgment methods re used to select the smple, the sttisticl bsis of the cceptnce-smpling procedure is lost. Definition of Single-Smpling Pln A single smpling pln is defined by smple size, n, nd the cceptnce number c. o N = lot size o n = smple size o c = cceptnce number o d = observed number of defectives N totl items in lot. Choose n of the items t rndom. If c or less number of items re defective, ccept the lot. The cceptnce or rejection of the lot is bsed on the results from single smple - thus single-smpling pln 115

4 The OC Curves The operting-chrcteristic (OC) curve mesures the performnce of n cceptnce-smpling pln. The OC curve plots the probbility of ccepting the lot versus the lot frction defective. P P{ d c} c d 0 n! p d!( n d)! d (1 p) n d Exmple. If the lot frction defective is p 0. 01, n=89 nd c=2, then P P{ d 2} 89! (0.01) 0!89! d 0 (0.99) 89! (0.01) d!(89 d)! 89 89! 1!88! (0.01) 1 d (0.99) (0.99) d 89! (0.01) 2!87! 2 (0.99) 87 The OC curve shows the probbility tht lot submitted with certin frction defective will be either ccepted or rejected. 116

5 Effect of OC curves 117

6 Type-A OC curves bsed on Hypergeometric distribution Type-B OC curves bsed on binomil distribution Other spects of OC Curve Behvior: 118

7 AQL nd LTPD Acceptble qulity level (AQL, p 1 ) - poorest level of qulity for the vendor s process tht the consumer would consider to be cceptble s process verge. The probbility of such process not being ccepted is the Producer s risk. Lot tolernce percent defective (LTPD, p 2 ) poorest level of qulity tht the consumer is willing to ccept in n individul lot. The probbility tht lot with lower qulity level is ccepted is the Consumer s risk. Also clled rejectble qulity level (RQL) or limiting qulity level (LQL) AQL nd LTPD cn be used for the design of smpling plns Designing Single-Smpling Pln with Specified OC Curve Let the probbility of cceptnce be 1 for lots with frction defective p 1 Let the probbility of cceptnce be for lots with frction defective p 2. Assume binomil smpling (with type-b OC curves). The smple size n nd cceptnce number c re the solution to n! c d n d 1 p1 (1 p1) d 0 d!( n d)! n! c d n d p (1 p d d!( n d)! )

8 Exmple. For p 1 = 0.01, =0.05 (or ), p 2 = 0.06, = 0.10, use computer softwre or grphicl pproch, it cn be shown tht the necessry vlues of n nd c re 89 nd 2, respectively. 120

9 Rectifying inspection Following prticulr smpling pln Accepted lots re pssed with non-conforming units replced Rejected lots re screened with 100% inspection, non-conforming units re replced The number of defective items pssing this rectifying inspection is: P p( N n) (1 P )0 P p( N n) The verge outgoing qulity (AOQ) of the lots pssing the inspection cn be clculted by: or simply use: P p( N n) AOQ N AOQ P where P is the cceptnce probbility, p is the frction defective, N is the btch size nd n is the smple size p 121

10 Exmple. N=10,000, n=89, c=2 nd p =0.01. From binomil distribution or the OC curve, we found tht P = Then: or simply: AOQ P p( N n) (0.9397)(0.01)(10,000 89) N 10,000 AOQ P p ( )(0.01) Averge outgoing qulity limit (AOQL) The worst possible verge qulity tht cn be resulted from the rectifying inspection progrm. Exmple. For rectifying inspection pln with n=89, c=2, we hve: p P AOQ p P AOQ

11 AOQL is the mximum point on the curve Averge totl inspection (ATI) per lot ATI n ( 1 P )( N n) Exmple. For N=10,000, n = 89, c = 2 nd p =0.01, we hve P = Then n ( 1 P )( N n) 89 ( )( ) 687 ATI 123

12 Double Smpling Plns Procedure o n 1 = smple size of the first smple o c 1 = cceptnce number of the first smple o n 2 = smple size of the second smple o c 2 = cceptnce number for both smples 124

13 Exmple. For the pln with n 1 =50, c 1 =1, n 2 =100, c 2 =3, rndom smple of 50 will be tken from the lot. If d 1 1, the lot will be ccepted. If d, the lot will be rejected. If d 2 or d 3, the second smple of will be tken. If d d 3, the lot will be ccepted. If d d 3, the lot will be rejected Remrks o Possible less inspection o Second chnce o More complicted o Less inspection my not be relized 125

14 Multiple Smpling Plns Similr to double smpling Possible less inspection More complicted My be further discussed lter 126

15 Militry Stndrd 105E (ANSI/ASQC Z1.4, ISO 2859) Developed during World Wr II Widely used cceptnce-smpling system for ttributes Gone through four revisions since collection of smpling schemes to mke smpling system Bsed on AQL Description of the Stndrd Three types of smpling re provided for: 1. Single 2. Double 3. Multiple Provisions for ech type of smpling pln include 1. Norml inspection 2. Tightened inspection 3. Reduced inspection The AQL is generlly specified in the contrct or by the uthority responsible for smpling Different AQLs my be designted for different types of defects Defects include criticl defects, mjor defects, nd minor defects Tbles re used to determine the pproprite smpling scheme 127

16 Switching Rules 1. Norml to tightened 2 out of five lots re rejected 2. Tightened to norml 5 lots re ccepted 3. Norml to reduced 10 lots hve been ccepted under norml inspection totl number of defectives of the 10 lots is less thn given limit stble production uthorized 4. Reduced to norml lot is rejected procedure termintes without meeting cceptnce or rejection criteri. Accept the lot nd chnge to norml production is not stble other conditions 5. Discontinunce of inspection 10 consecutive lots remin on tightened inspection. Discontinue nd tke ctions. 128

17 Procedure 1. Choose the AQL 2. Choose the inspection level 3. Determine the lot size 4. Find the pproprite smple size code letter from Tble Determine the pproprite type of smpling pln to use (single, double, multiple) 6. Enter the pproprite tble to find the type of pln to be used. 7. Determine the corresponding norml nd reduced inspection plns to be used when required 129

18 Exmple Suppose product is submitted in lots of size N = The AQL is 0.65%. Assume tht we wnt to generte norml singlesmpling plns. For lots of size 2000 nd generl inspection level II, Tble 15-4 indictes the pproprite smple size code letter is K. From Tble 15-5 for single-smpling plns under norml inspection, the norml inspection pln is n = 125, c = 2. This mens tht we ccept the lot if there re 2 or less defective units in rndom smple of 125. We reject the lot if there re 3 or more defective units. If tightened inspection is to be used fter inspecting 5 lots with norml inspection, then Tble 15-6 shows tht n = 125, c =1 for tightened inspection. This mens tht we ccept the lot if there is 1 or 0 defective units in rndom smple of 125. We reject the lot if there re 2 or more defective units. If reduced inspection cn be used fter ccepting 10 consecutive lots with norml inspection, nd ll other conditions stisfied, then Tble 15-7 shows tht in the reduced inspection, the smple size is n =50 Ac=1 nd Re=3. This mens tht: If there re 1 or less defectives in the smple, we will ccept the lot If there re 3 or more defective units, we will reject the lot nd use norml inspection for inspecting the next lot. If there re 2 defective units, we will ccept the lot nd use norml inspection for inspecting the next lot. 130

19 131

20 132

21 Discussion Severl points to be emphsized: MIL STD 105E is AQL-oriented The smple sizes selected for use in MIL STD 105E re limited The smple sizes re relted to the lot sizes. Switching rules from norml to tightened nd from tightened to norml re subject to some criticism. A common buse of the stndrd is filure to use the switching rules t ll. ANSI/ASQC Z1.4 or ISO 2859 is the civilin stndrd counterprt of MIL STD 105E. Differences include: 1. Terminology nonconformity, nonconformnce, nd percent nonconforming is used 2. Switching rules were chnged slightly to provide n option for reduced inspection without the use of limit numbers 3. Severl tbles tht show mesures of scheme performnce were introduced 4. A section ws dded describing proper use of individul smpling plns when extrcted from the system 5. A figure illustrting switching rules ws dded 133

22 Dodge-Romig Smpling Plns Bsed on AOQL or LTPD Use developed tbles AOQL plns o Minimize verge totl inspection o Rejected lots will be 100% inspected o Frction nonconforming is known or cn be estimted o The pln lso presents the LTPD vlues corresponding to P 0.10 on the OC curve of the pln. Or 90% of the lots will be rejected if its percent defective is higher thn the corresponding LTPD vlue. Exmple. N=5000, p=1%. From Tble 15-8, we find tht the pln with AOQL=3% will be n=65, c=3. The corresponding LTPD vlue t P 0.10 is 10.3%. 134

23 If the incoming qulity is indeed t the level of p=1%, we cn clculte or check the corresponding OC curve to see tht the cceptnce probbility is P for p=1%. Then the verge totl inspection will be: n ( 1 P )( N n) 65 ( )( ) ATI LTPD plns o Provide plns for different LTPD vlues with lot cceptnce probbility of 10%. Exmple. N=5000 nd incoming percent defective is p=0.25%. We cn find from Tble 15-9 tht the pln with LTPD=1% will be n=770, c=4. The corresponding AOQL vlue for this pln is 0.28%. 135

24 Similrly, we cn find tht if indeed tht the incoming percent defective is p=0.25%, we cn clculte or check the corresponding OC curve to see tht the cceptnce probbility is P for p=0.25%. Then the verge totl inspection will be: ATI n ( 1 P )( N n) 770 ( )( ) Item-by-Item Sequentil Smpling Plns The procedure is to tke one unit from the lot to test t one time nd continue for number of items. Bsed on the test results, the entire lot will be ccepted or rejected. In doing so, it my reduce the totl number of items to be tested. This procedure is lso indexed on p 1, p 2 nd. It clcultes the following 2 lines for ech smple: X A X R h1 sn (cceptnce line) h sn 2 (rejection line) 136

25 If the totl number of nonconforming units is less thn or equl to the integer prt of X, we ccept the lot. A If the totl number of nonconforming units is equl to or greter thn the integer bove X, we reject the lot. R These prmeters re clculted by: 1 1 h 1 log / k, h 2 log / k 1 p 1 s log / k 1 p 2, k p2(1 p1) log nd p (1 p ) 1 2 Exmple. Assume tht p 1 =0.01, =0.05, p 2 =0.06 nd =0.10, then: p (1 p1) k log log p (1 p ) h1 log / k log / h2 log / k log / p s log / log / k p Then we hve: X A X R h1 sn = n h 2 sn n (cceptnce line) (rejection line) When n = 1, we hve: X A X R h1 sn = n = h 2 sn n

26 As we just tke one smple, these results tell us nothing. When n = 2, we hve: h1 sn = X A X R h 2 sn So it sys nothing bout ccepting the lot but the lot will be rejected if both items re bd. For this pln, the process continues for X A until n = 44 when X A becomes positive. On the other hnd, the rejection numbers re shown in Tble As the process continues, if there re int[ X R ] 1 bd ones, reject the lot. If the first 44 re ll good ones, ccept the lot. Otherwise, continue nd stop smpling when you rech items. The number of 89 corresponds to the single smpling pln for p 1 =0.01, =0.05, p 2 =0.06 nd =

27 A similr exmple is to ssume tht p 1 =0.01, =0.05, p 2 =0.10 nd =0.10. In this cse, we hve: X A X R h1 sn = n h 2 sn n (cceptnce line) (rejection line) The results for n =1, 2, re tbulted below. n ccept reject n ccept reject 1 x x 14 x 2 2 x 2 15 x 2 3 x 2 16 x 3 4 x 2 17 x 3 5 x 2 18 x 3 6 x 2 19 x 3 7 x 2 20 x 3 8 x 2 21 x 3 9 x 2 22 x 3 10 x 2 23 x 3 11 x x x If it continues, we hve the following vlues: n Acceptnce Rejection The corresponding single smpling pln is (52, 2) nd the smpling my stop fter 156 smple items re tken. 139

Construction and Selection of Single Sampling Quick Switching Variables System for given Control Limits Involving Minimum Sum of Risks

Construction and Selection of Single Sampling Quick Switching Variables System for given Control Limits Involving Minimum Sum of Risks Construction nd Selection of Single Smpling Quick Switching Vribles System for given Control Limits Involving Minimum Sum of Risks Dr. D. SENHILKUMAR *1 R. GANESAN B. ESHA RAFFIE 1 Associte Professor,

More information

Hybrid Group Acceptance Sampling Plan Based on Size Biased Lomax Model

Hybrid Group Acceptance Sampling Plan Based on Size Biased Lomax Model Mthemtics nd Sttistics 2(3): 137-141, 2014 DOI: 10.13189/ms.2014.020305 http://www.hrpub.org Hybrid Group Acceptnce Smpling Pln Bsed on Size Bised Lomx Model R. Subb Ro 1,*, A. Ng Durgmmb 2, R.R.L. Kntm

More information

Time Truncated Two Stage Group Sampling Plan For Various Distributions

Time Truncated Two Stage Group Sampling Plan For Various Distributions Time Truncted Two Stge Group Smpling Pln For Vrious Distributions Dr. A. R. Sudmni Rmswmy, S.Jysri Associte Professor, Deprtment of Mthemtics, Avinshilingm University, Coimbtore Assistnt professor, Deprtment

More information

Tests for the Ratio of Two Poisson Rates

Tests for the Ratio of Two Poisson Rates Chpter 437 Tests for the Rtio of Two Poisson Rtes Introduction The Poisson probbility lw gives the probbility distribution of the number of events occurring in specified intervl of time or spce. The Poisson

More information

Lecture 3 Gaussian Probability Distribution

Lecture 3 Gaussian Probability Distribution Introduction Lecture 3 Gussin Probbility Distribution Gussin probbility distribution is perhps the most used distribution in ll of science. lso clled bell shped curve or norml distribution Unlike the binomil

More information

Learning Objectives 15.1 The Acceptance-Sampling Problem

Learning Objectives 15.1 The Acceptance-Sampling Problem Learning Objectives 5. The Acceptance-Sampling Problem Acceptance sampling plan (ASP): ASP is a specific plan that clearly states the rules for sampling and the associated criteria for acceptance or otherwise.

More information

The steps of the hypothesis test

The steps of the hypothesis test ttisticl Methods I (EXT 7005) Pge 78 Mosquito species Time of dy A B C Mid morning 0.0088 5.4900 5.5000 Mid Afternoon.3400 0.0300 0.8700 Dusk 0.600 5.400 3.000 The Chi squre test sttistic is the sum of

More information

Chapter 9: Inferences based on Two samples: Confidence intervals and tests of hypotheses

Chapter 9: Inferences based on Two samples: Confidence intervals and tests of hypotheses Chpter 9: Inferences bsed on Two smples: Confidence intervls nd tests of hypotheses 9.1 The trget prmeter : difference between two popultion mens : difference between two popultion proportions : rtio of

More information

Estimation of Binomial Distribution in the Light of Future Data

Estimation of Binomial Distribution in the Light of Future Data British Journl of Mthemtics & Computer Science 102: 1-7, 2015, Article no.bjmcs.19191 ISSN: 2231-0851 SCIENCEDOMAIN interntionl www.sciencedomin.org Estimtion of Binomil Distribution in the Light of Future

More information

SUMMER KNOWHOW STUDY AND LEARNING CENTRE

SUMMER KNOWHOW STUDY AND LEARNING CENTRE SUMMER KNOWHOW STUDY AND LEARNING CENTRE Indices & Logrithms 2 Contents Indices.2 Frctionl Indices.4 Logrithms 6 Exponentil equtions. Simplifying Surds 13 Opertions on Surds..16 Scientific Nottion..18

More information

New data structures to reduce data size and search time

New data structures to reduce data size and search time New dt structures to reduce dt size nd serch time Tsuneo Kuwbr Deprtment of Informtion Sciences, Fculty of Science, Kngw University, Hirtsuk-shi, Jpn FIT2018 1D-1, No2, pp1-4 Copyright (c)2018 by The Institute

More information

20 MATHEMATICS POLYNOMIALS

20 MATHEMATICS POLYNOMIALS 0 MATHEMATICS POLYNOMIALS.1 Introduction In Clss IX, you hve studied polynomils in one vrible nd their degrees. Recll tht if p(x) is polynomil in x, the highest power of x in p(x) is clled the degree of

More information

CS667 Lecture 6: Monte Carlo Integration 02/10/05

CS667 Lecture 6: Monte Carlo Integration 02/10/05 CS667 Lecture 6: Monte Crlo Integrtion 02/10/05 Venkt Krishnrj Lecturer: Steve Mrschner 1 Ide The min ide of Monte Crlo Integrtion is tht we cn estimte the vlue of n integrl by looking t lrge number of

More information

Goals: Determine how to calculate the area described by a function. Define the definite integral. Explore the relationship between the definite

Goals: Determine how to calculate the area described by a function. Define the definite integral. Explore the relationship between the definite Unit #8 : The Integrl Gols: Determine how to clculte the re described by function. Define the definite integrl. Eplore the reltionship between the definite integrl nd re. Eplore wys to estimte the definite

More information

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives Block #6: Properties of Integrls, Indefinite Integrls Gols: Definition of the Definite Integrl Integrl Clcultions using Antiderivtives Properties of Integrls The Indefinite Integrl 1 Riemnn Sums - 1 Riemnn

More information

Section 11.5 Estimation of difference of two proportions

Section 11.5 Estimation of difference of two proportions ection.5 Estimtion of difference of two proportions As seen in estimtion of difference of two mens for nonnorml popultion bsed on lrge smple sizes, one cn use CLT in the pproximtion of the distribution

More information

fractions Let s Learn to

fractions Let s Learn to 5 simple lgebric frctions corne lens pupil retin Norml vision light focused on the retin concve lens Shortsightedness (myopi) light focused in front of the retin Corrected myopi light focused on the retin

More information

Credibility Hypothesis Testing of Fuzzy Triangular Distributions

Credibility Hypothesis Testing of Fuzzy Triangular Distributions 666663 Journl of Uncertin Systems Vol.9, No., pp.6-74, 5 Online t: www.jus.org.uk Credibility Hypothesis Testing of Fuzzy Tringulr Distributions S. Smpth, B. Rmy Received April 3; Revised 4 April 4 Abstrct

More information

CS 188 Introduction to Artificial Intelligence Fall 2018 Note 7

CS 188 Introduction to Artificial Intelligence Fall 2018 Note 7 CS 188 Introduction to Artificil Intelligence Fll 2018 Note 7 These lecture notes re hevily bsed on notes originlly written by Nikhil Shrm. Decision Networks In the third note, we lerned bout gme trees

More information

Numerical integration

Numerical integration 2 Numericl integrtion This is pge i Printer: Opque this 2. Introduction Numericl integrtion is problem tht is prt of mny problems in the economics nd econometrics literture. The orgniztion of this chpter

More information

Math 1B, lecture 4: Error bounds for numerical methods

Math 1B, lecture 4: Error bounds for numerical methods Mth B, lecture 4: Error bounds for numericl methods Nthn Pflueger 4 September 0 Introduction The five numericl methods descried in the previous lecture ll operte by the sme principle: they pproximte the

More information

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below.

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below. Dulity #. Second itertion for HW problem Recll our LP emple problem we hve been working on, in equlity form, is given below.,,,, 8 m F which, when written in slightly different form, is 8 F Recll tht we

More information

For the percentage of full time students at RCC the symbols would be:

For the percentage of full time students at RCC the symbols would be: Mth 17/171 Chpter 7- ypothesis Testing with One Smple This chpter is s simple s the previous one, except it is more interesting In this chpter we will test clims concerning the sme prmeters tht we worked

More information

19 Optimal behavior: Game theory

19 Optimal behavior: Game theory Intro. to Artificil Intelligence: Dle Schuurmns, Relu Ptrscu 1 19 Optiml behvior: Gme theory Adversril stte dynmics hve to ccount for worst cse Compute policy π : S A tht mximizes minimum rewrd Let S (,

More information

Chapter 0. What is the Lebesgue integral about?

Chapter 0. What is the Lebesgue integral about? Chpter 0. Wht is the Lebesgue integrl bout? The pln is to hve tutoril sheet ech week, most often on Fridy, (to be done during the clss) where you will try to get used to the ides introduced in the previous

More information

MATH 144: Business Calculus Final Review

MATH 144: Business Calculus Final Review MATH 144: Business Clculus Finl Review 1 Skills 1. Clculte severl limits. 2. Find verticl nd horizontl symptotes for given rtionl function. 3. Clculte derivtive by definition. 4. Clculte severl derivtives

More information

Math Lecture 23

Math Lecture 23 Mth 8 - Lecture 3 Dyln Zwick Fll 3 In our lst lecture we delt with solutions to the system: x = Ax where A is n n n mtrix with n distinct eigenvlues. As promised, tody we will del with the question of

More information

Reinforcement learning II

Reinforcement learning II CS 1675 Introduction to Mchine Lerning Lecture 26 Reinforcement lerning II Milos Huskrecht milos@cs.pitt.edu 5329 Sennott Squre Reinforcement lerning Bsics: Input x Lerner Output Reinforcement r Critic

More information

Chapter 5 : Continuous Random Variables

Chapter 5 : Continuous Random Variables STAT/MATH 395 A - PROBABILITY II UW Winter Qurter 216 Néhémy Lim Chpter 5 : Continuous Rndom Vribles Nottions. N {, 1, 2,...}, set of nturl numbers (i.e. ll nonnegtive integers); N {1, 2,...}, set of ll

More information

Continuous Random Variables

Continuous Random Variables STAT/MATH 395 A - PROBABILITY II UW Winter Qurter 217 Néhémy Lim Continuous Rndom Vribles Nottion. The indictor function of set S is rel-vlued function defined by : { 1 if x S 1 S (x) if x S Suppose tht

More information

NUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by.

NUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by. NUMERICAL INTEGRATION 1 Introduction The inverse process to differentition in clculus is integrtion. Mthemticlly, integrtion is represented by f(x) dx which stnds for the integrl of the function f(x) with

More information

Genetic Programming. Outline. Evolutionary Strategies. Evolutionary strategies Genetic programming Summary

Genetic Programming. Outline. Evolutionary Strategies. Evolutionary strategies Genetic programming Summary Outline Genetic Progrmming Evolutionry strtegies Genetic progrmming Summry Bsed on the mteril provided y Professor Michel Negnevitsky Evolutionry Strtegies An pproch simulting nturl evolution ws proposed

More information

The graphs of Rational Functions

The graphs of Rational Functions Lecture 4 5A: The its of Rtionl Functions s x nd s x + The grphs of Rtionl Functions The grphs of rtionl functions hve severl differences compred to power functions. One of the differences is the behvior

More information

Infinite Geometric Series

Infinite Geometric Series Infinite Geometric Series Finite Geometric Series ( finite SUM) Let 0 < r < 1, nd let n be positive integer. Consider the finite sum It turns out there is simple lgebric expression tht is equivlent to

More information

Math 113 Fall Final Exam Review. 2. Applications of Integration Chapter 6 including sections and section 6.8

Math 113 Fall Final Exam Review. 2. Applications of Integration Chapter 6 including sections and section 6.8 Mth 3 Fll 0 The scope of the finl exm will include: Finl Exm Review. Integrls Chpter 5 including sections 5. 5.7, 5.0. Applictions of Integrtion Chpter 6 including sections 6. 6.5 nd section 6.8 3. Infinite

More information

1 Probability Density Functions

1 Probability Density Functions Lis Yn CS 9 Continuous Distributions Lecture Notes #9 July 6, 28 Bsed on chpter by Chris Piech So fr, ll rndom vribles we hve seen hve been discrete. In ll the cses we hve seen in CS 9, this ment tht our

More information

5.7 Improper Integrals

5.7 Improper Integrals 458 pplictions of definite integrls 5.7 Improper Integrls In Section 5.4, we computed the work required to lift pylod of mss m from the surfce of moon of mss nd rdius R to height H bove the surfce of the

More information

Vyacheslav Telnin. Search for New Numbers.

Vyacheslav Telnin. Search for New Numbers. Vycheslv Telnin Serch for New Numbers. 1 CHAPTER I 2 I.1 Introduction. In 1984, in the first issue for tht yer of the Science nd Life mgzine, I red the rticle "Non-Stndrd Anlysis" by V. Uspensky, in which

More information

Chapters 4 & 5 Integrals & Applications

Chapters 4 & 5 Integrals & Applications Contents Chpters 4 & 5 Integrls & Applictions Motivtion to Chpters 4 & 5 2 Chpter 4 3 Ares nd Distnces 3. VIDEO - Ares Under Functions............................................ 3.2 VIDEO - Applictions

More information

Monte Carlo method in solving numerical integration and differential equation

Monte Carlo method in solving numerical integration and differential equation Monte Crlo method in solving numericl integrtion nd differentil eqution Ye Jin Chemistry Deprtment Duke University yj66@duke.edu Abstrct: Monte Crlo method is commonly used in rel physics problem. The

More information

Driving Cycle Construction of City Road for Hybrid Bus Based on Markov Process Deng Pan1, a, Fengchun Sun1,b*, Hongwen He1, c, Jiankun Peng1, d

Driving Cycle Construction of City Road for Hybrid Bus Based on Markov Process Deng Pan1, a, Fengchun Sun1,b*, Hongwen He1, c, Jiankun Peng1, d Interntionl Industril Informtics nd Computer Engineering Conference (IIICEC 15) Driving Cycle Construction of City Rod for Hybrid Bus Bsed on Mrkov Process Deng Pn1,, Fengchun Sun1,b*, Hongwen He1, c,

More information

Section 5.1 #7, 10, 16, 21, 25; Section 5.2 #8, 9, 15, 20, 27, 30; Section 5.3 #4, 6, 9, 13, 16, 28, 31; Section 5.4 #7, 18, 21, 23, 25, 29, 40

Section 5.1 #7, 10, 16, 21, 25; Section 5.2 #8, 9, 15, 20, 27, 30; Section 5.3 #4, 6, 9, 13, 16, 28, 31; Section 5.4 #7, 18, 21, 23, 25, 29, 40 Mth B Prof. Audrey Terrs HW # Solutions by Alex Eustis Due Tuesdy, Oct. 9 Section 5. #7,, 6,, 5; Section 5. #8, 9, 5,, 7, 3; Section 5.3 #4, 6, 9, 3, 6, 8, 3; Section 5.4 #7, 8,, 3, 5, 9, 4 5..7 Since

More information

Student Activity 3: Single Factor ANOVA

Student Activity 3: Single Factor ANOVA MATH 40 Student Activity 3: Single Fctor ANOVA Some Bsic Concepts In designed experiment, two or more tretments, or combintions of tretments, is pplied to experimentl units The number of tretments, whether

More information

Review of Calculus, cont d

Review of Calculus, cont d Jim Lmbers MAT 460 Fll Semester 2009-10 Lecture 3 Notes These notes correspond to Section 1.1 in the text. Review of Clculus, cont d Riemnn Sums nd the Definite Integrl There re mny cses in which some

More information

Riemann is the Mann! (But Lebesgue may besgue to differ.)

Riemann is the Mann! (But Lebesgue may besgue to differ.) Riemnn is the Mnn! (But Lebesgue my besgue to differ.) Leo Livshits My 2, 2008 1 For finite intervls in R We hve seen in clss tht every continuous function f : [, b] R hs the property tht for every ɛ >

More information

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac REVIEW OF ALGEBRA Here we review the bsic rules nd procedures of lgebr tht you need to know in order to be successful in clculus. ARITHMETIC OPERATIONS The rel numbers hve the following properties: b b

More information

Fig. 1. Open-Loop and Closed-Loop Systems with Plant Variations

Fig. 1. Open-Loop and Closed-Loop Systems with Plant Variations ME 3600 Control ystems Chrcteristics of Open-Loop nd Closed-Loop ystems Importnt Control ystem Chrcteristics o ensitivity of system response to prmetric vritions cn be reduced o rnsient nd stedy-stte responses

More information

UNIT 1 FUNCTIONS AND THEIR INVERSES Lesson 1.4: Logarithmic Functions as Inverses Instruction

UNIT 1 FUNCTIONS AND THEIR INVERSES Lesson 1.4: Logarithmic Functions as Inverses Instruction Lesson : Logrithmic Functions s Inverses Prerequisite Skills This lesson requires the use of the following skills: determining the dependent nd independent vribles in n exponentil function bsed on dt from

More information

1 Online Learning and Regret Minimization

1 Online Learning and Regret Minimization 2.997 Decision-Mking in Lrge-Scle Systems My 10 MIT, Spring 2004 Hndout #29 Lecture Note 24 1 Online Lerning nd Regret Minimiztion In this lecture, we consider the problem of sequentil decision mking in

More information

Matching with Multiple Applications: The Limiting Case

Matching with Multiple Applications: The Limiting Case Mtching with Multiple Applictions: The Limiting Cse Jmes W. Albrecht y Georgetown Uniersity Susn B. Vromn Georgetown Uniersity August 003 Pieter A. Gutier Ersmus Uniersity Tinbergen Institute Abstrct We

More information

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique? XII. LINEAR ALGEBRA: SOLVING SYSTEMS OF EQUATIONS Tody we re going to tlk bout solving systems of liner equtions. These re problems tht give couple of equtions with couple of unknowns, like: 6 2 3 7 4

More information

APPROXIMATE INTEGRATION

APPROXIMATE INTEGRATION APPROXIMATE INTEGRATION. Introduction We hve seen tht there re functions whose nti-derivtives cnnot be expressed in closed form. For these resons ny definite integrl involving these integrnds cnnot be

More information

7.2 The Definite Integral

7.2 The Definite Integral 7.2 The Definite Integrl the definite integrl In the previous section, it ws found tht if function f is continuous nd nonnegtive, then the re under the grph of f on [, b] is given by F (b) F (), where

More information

p-adic Egyptian Fractions

p-adic Egyptian Fractions p-adic Egyptin Frctions Contents 1 Introduction 1 2 Trditionl Egyptin Frctions nd Greedy Algorithm 2 3 Set-up 3 4 p-greedy Algorithm 5 5 p-egyptin Trditionl 10 6 Conclusion 1 Introduction An Egyptin frction

More information

University of Texas MD Anderson Cancer Center Department of Biostatistics. Inequality Calculator, Version 3.0 November 25, 2013 User s Guide

University of Texas MD Anderson Cancer Center Department of Biostatistics. Inequality Calculator, Version 3.0 November 25, 2013 User s Guide University of Texs MD Anderson Cncer Center Deprtment of Biosttistics Inequlity Clcultor, Version 3.0 November 5, 013 User s Guide 0. Overview The purpose of the softwre is to clculte the probbility tht

More information

Reinforcement Learning

Reinforcement Learning Reinforcement Lerning Tom Mitchell, Mchine Lerning, chpter 13 Outline Introduction Comprison with inductive lerning Mrkov Decision Processes: the model Optiml policy: The tsk Q Lerning: Q function Algorithm

More information

A BRIEF INTRODUCTION TO UNIFORM CONVERGENCE. In the study of Fourier series, several questions arise naturally, such as: c n e int

A BRIEF INTRODUCTION TO UNIFORM CONVERGENCE. In the study of Fourier series, several questions arise naturally, such as: c n e int A BRIEF INTRODUCTION TO UNIFORM CONVERGENCE HANS RINGSTRÖM. Questions nd exmples In the study of Fourier series, severl questions rise nturlly, such s: () (2) re there conditions on c n, n Z, which ensure

More information

UNIFORM CONVERGENCE. Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3

UNIFORM CONVERGENCE. Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3 UNIFORM CONVERGENCE Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3 Suppose f n : Ω R or f n : Ω C is sequence of rel or complex functions, nd f n f s n in some sense. Furthermore,

More information

4.4 Areas, Integrals and Antiderivatives

4.4 Areas, Integrals and Antiderivatives . res, integrls nd ntiderivtives 333. Ares, Integrls nd Antiderivtives This section explores properties of functions defined s res nd exmines some connections mong res, integrls nd ntiderivtives. In order

More information

Math 8 Winter 2015 Applications of Integration

Math 8 Winter 2015 Applications of Integration Mth 8 Winter 205 Applictions of Integrtion Here re few importnt pplictions of integrtion. The pplictions you my see on n exm in this course include only the Net Chnge Theorem (which is relly just the Fundmentl

More information

Chapter 1: Fundamentals

Chapter 1: Fundamentals Chpter 1: Fundmentls 1.1 Rel Numbers Types of Rel Numbers: Nturl Numbers: {1, 2, 3,...}; These re the counting numbers. Integers: {... 3, 2, 1, 0, 1, 2, 3,...}; These re ll the nturl numbers, their negtives,

More information

The area under the graph of f and above the x-axis between a and b is denoted by. f(x) dx. π O

The area under the graph of f and above the x-axis between a and b is denoted by. f(x) dx. π O 1 Section 5. The Definite Integrl Suppose tht function f is continuous nd positive over n intervl [, ]. y = f(x) x The re under the grph of f nd ove the x-xis etween nd is denoted y f(x) dx nd clled the

More information

Discrete Mathematics and Probability Theory Summer 2014 James Cook Note 17

Discrete Mathematics and Probability Theory Summer 2014 James Cook Note 17 CS 70 Discrete Mthemtics nd Proility Theory Summer 2014 Jmes Cook Note 17 I.I.D. Rndom Vriles Estimting the is of coin Question: We wnt to estimte the proportion p of Democrts in the US popultion, y tking

More information

MAA 4212 Improper Integrals

MAA 4212 Improper Integrals Notes by Dvid Groisser, Copyright c 1995; revised 2002, 2009, 2014 MAA 4212 Improper Integrls The Riemnn integrl, while perfectly well-defined, is too restrictive for mny purposes; there re functions which

More information

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions Physics 6C Solution of inhomogeneous ordinry differentil equtions using Green s functions Peter Young November 5, 29 Homogeneous Equtions We hve studied, especilly in long HW problem, second order liner

More information

Unit #9 : Definite Integral Properties; Fundamental Theorem of Calculus

Unit #9 : Definite Integral Properties; Fundamental Theorem of Calculus Unit #9 : Definite Integrl Properties; Fundmentl Theorem of Clculus Gols: Identify properties of definite integrls Define odd nd even functions, nd reltionship to integrl vlues Introduce the Fundmentl

More information

Improper Integrals. Type I Improper Integrals How do we evaluate an integral such as

Improper Integrals. Type I Improper Integrals How do we evaluate an integral such as Improper Integrls Two different types of integrls cn qulify s improper. The first type of improper integrl (which we will refer to s Type I) involves evluting n integrl over n infinite region. In the grph

More information

Best Approximation. Chapter The General Case

Best Approximation. Chapter The General Case Chpter 4 Best Approximtion 4.1 The Generl Cse In the previous chpter, we hve seen how n interpolting polynomil cn be used s n pproximtion to given function. We now wnt to find the best pproximtion to given

More information

Discrete Mathematics and Probability Theory Spring 2013 Anant Sahai Lecture 17

Discrete Mathematics and Probability Theory Spring 2013 Anant Sahai Lecture 17 EECS 70 Discrete Mthemtics nd Proility Theory Spring 2013 Annt Shi Lecture 17 I.I.D. Rndom Vriles Estimting the is of coin Question: We wnt to estimte the proportion p of Democrts in the US popultion,

More information

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams Chpter 4 Contrvrince, Covrince, nd Spcetime Digrms 4. The Components of Vector in Skewed Coordintes We hve seen in Chpter 3; figure 3.9, tht in order to show inertil motion tht is consistent with the Lorentz

More information

ODE: Existence and Uniqueness of a Solution

ODE: Existence and Uniqueness of a Solution Mth 22 Fll 213 Jerry Kzdn ODE: Existence nd Uniqueness of Solution The Fundmentl Theorem of Clculus tells us how to solve the ordinry differentil eqution (ODE) du = f(t) dt with initil condition u() =

More information

CS 275 Automata and Formal Language Theory

CS 275 Automata and Formal Language Theory CS 275 Automt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Problem (II) Chpter II.6.: Push Down Automt Remrk: This mteril is no longer tught nd not directly exm relevnt Anton Setzer (Bsed

More information

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata CS103B ndout 18 Winter 2007 Ferury 28, 2007 Finite Automt Initil text y Mggie Johnson. Introduction Severl childrens gmes fit the following description: Pieces re set up on plying ord; dice re thrown or

More information

Sample pages. 9:04 Equations with grouping symbols

Sample pages. 9:04 Equations with grouping symbols Equtions 9 Contents I know the nswer is here somewhere! 9:01 Inverse opertions 9:0 Solving equtions Fun spot 9:0 Why did the tooth get dressed up? 9:0 Equtions with pronumerls on both sides GeoGebr ctivity

More information

Strategy: Use the Gibbs phase rule (Equation 5.3). How many components are present?

Strategy: Use the Gibbs phase rule (Equation 5.3). How many components are present? University Chemistry Quiz 4 2014/12/11 1. (5%) Wht is the dimensionlity of the three-phse coexistence region in mixture of Al, Ni, nd Cu? Wht type of geometricl region dose this define? Strtegy: Use the

More information

Theoretical foundations of Gaussian quadrature

Theoretical foundations of Gaussian quadrature Theoreticl foundtions of Gussin qudrture 1 Inner product vector spce Definition 1. A vector spce (or liner spce) is set V = {u, v, w,...} in which the following two opertions re defined: (A) Addition of

More information

Recitation 3: More Applications of the Derivative

Recitation 3: More Applications of the Derivative Mth 1c TA: Pdric Brtlett Recittion 3: More Applictions of the Derivtive Week 3 Cltech 2012 1 Rndom Question Question 1 A grph consists of the following: A set V of vertices. A set E of edges where ech

More information

CHM Physical Chemistry I Chapter 1 - Supplementary Material

CHM Physical Chemistry I Chapter 1 - Supplementary Material CHM 3410 - Physicl Chemistry I Chpter 1 - Supplementry Mteril For review of some bsic concepts in mth, see Atkins "Mthemticl Bckground 1 (pp 59-6), nd "Mthemticl Bckground " (pp 109-111). 1. Derivtion

More information

ME 418 Quality in Manufacturing ISE Quality Control and Industrial Statistics CHAPTER 07 ACCEPTANCE SAMPLING PLANS.

ME 418 Quality in Manufacturing ISE Quality Control and Industrial Statistics CHAPTER 07 ACCEPTANCE SAMPLING PLANS. University of Hail College of Engineering ME 418 Quality in Manufacturing ISE 320 - Quality Control and Industrial Statistics CHAPTER 07 ACCEPTANCE SAMPLING PLANS Professor Mohamed Aichouni http://cutt.us/maichouni

More information

CMDA 4604: Intermediate Topics in Mathematical Modeling Lecture 19: Interpolation and Quadrature

CMDA 4604: Intermediate Topics in Mathematical Modeling Lecture 19: Interpolation and Quadrature CMDA 4604: Intermedite Topics in Mthemticl Modeling Lecture 19: Interpoltion nd Qudrture In this lecture we mke brief diversion into the res of interpoltion nd qudrture. Given function f C[, b], we sy

More information

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4 Intermedite Mth Circles Wednesdy, Novemer 14, 2018 Finite Automt II Nickols Rollick nrollick@uwterloo.c Regulr Lnguges Lst time, we were introduced to the ide of DFA (deterministic finite utomton), one

More information

Chapter 14. Matrix Representations of Linear Transformations

Chapter 14. Matrix Representations of Linear Transformations Chpter 4 Mtrix Representtions of Liner Trnsformtions When considering the Het Stte Evolution, we found tht we could describe this process using multipliction by mtrix. This ws nice becuse computers cn

More information

Comparison Procedures

Comparison Procedures Comprison Procedures Single Fctor, Between-Subects Cse /8/ Comprison Procedures, One-Fctor ANOVA, Between Subects Two Comprison Strtegies post hoc (fter-the-fct) pproch You re interested in discovering

More information

Measuring Electron Work Function in Metal

Measuring Electron Work Function in Metal n experiment of the Electron topic Mesuring Electron Work Function in Metl Instructor: 梁生 Office: 7-318 Emil: shling@bjtu.edu.cn Purposes 1. To understnd the concept of electron work function in metl nd

More information

Continuous Random Variables Class 5, Jeremy Orloff and Jonathan Bloom

Continuous Random Variables Class 5, Jeremy Orloff and Jonathan Bloom Lerning Gols Continuous Rndom Vriles Clss 5, 8.05 Jeremy Orloff nd Jonthn Bloom. Know the definition of continuous rndom vrile. 2. Know the definition of the proility density function (pdf) nd cumultive

More information

f(x) dx, If one of these two conditions is not met, we call the integral improper. Our usual definition for the value for the definite integral

f(x) dx, If one of these two conditions is not met, we call the integral improper. Our usual definition for the value for the definite integral Improper Integrls Every time tht we hve evluted definite integrl such s f(x) dx, we hve mde two implicit ssumptions bout the integrl:. The intervl [, b] is finite, nd. f(x) is continuous on [, b]. If one

More information

Generalized Fano and non-fano networks

Generalized Fano and non-fano networks Generlized Fno nd non-fno networks Nildri Ds nd Brijesh Kumr Ri Deprtment of Electronics nd Electricl Engineering Indin Institute of Technology Guwhti, Guwhti, Assm, Indi Emil: {d.nildri, bkri}@iitg.ernet.in

More information

Chapter 5. Numerical Integration

Chapter 5. Numerical Integration Chpter 5. Numericl Integrtion These re just summries of the lecture notes, nd few detils re included. Most of wht we include here is to be found in more detil in Anton. 5. Remrk. There re two topics with

More information

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies Stte spce systems nlysis (continued) Stbility A. Definitions A system is sid to be Asymptoticlly Stble (AS) when it stisfies ut () = 0, t > 0 lim xt () 0. t A system is AS if nd only if the impulse response

More information

Mapping the delta function and other Radon measures

Mapping the delta function and other Radon measures Mpping the delt function nd other Rdon mesures Notes for Mth583A, Fll 2008 November 25, 2008 Rdon mesures Consider continuous function f on the rel line with sclr vlues. It is sid to hve bounded support

More information

Bernoulli Numbers Jeff Morton

Bernoulli Numbers Jeff Morton Bernoulli Numbers Jeff Morton. We re interested in the opertor e t k d k t k, which is to sy k tk. Applying this to some function f E to get e t f d k k tk d k f f + d k k tk dk f, we note tht since f

More information

Continuous Random Variable X:

Continuous Random Variable X: Continuous Rndom Vrile : The continuous rndom vrile hs its vlues in n intervl, nd it hs proility distriution unction or proility density unction p.d. stisies:, 0 & d Which does men tht the totl re under

More information

along the vector 5 a) Find the plane s coordinate after 1 hour. b) Find the plane s coordinate after 2 hours. c) Find the plane s coordinate

along the vector 5 a) Find the plane s coordinate after 1 hour. b) Find the plane s coordinate after 2 hours. c) Find the plane s coordinate L8 VECTOR EQUATIONS OF LINES HL Mth - Sntowski Vector eqution of line 1 A plne strts journey t the point (4,1) moves ech hour long the vector. ) Find the plne s coordinte fter 1 hour. b) Find the plne

More information

Sections 5.2: The Definite Integral

Sections 5.2: The Definite Integral Sections 5.2: The Definite Integrl In this section we shll formlize the ides from the lst section to functions in generl. We strt with forml definition.. The Definite Integrl Definition.. Suppose f(x)

More information

Lesson 1.6 Exercises, pages 68 73

Lesson 1.6 Exercises, pages 68 73 Lesson.6 Exercises, pges 68 7 A. Determine whether ech infinite geometric series hs finite sum. How do you know? ) + +.5 + 6.75 +... r is:.5, so the sum is not finite. b) 0.5 0.05 0.005 0.0005... r is:

More information

LECTURE 14. Dr. Teresa D. Golden University of North Texas Department of Chemistry

LECTURE 14. Dr. Teresa D. Golden University of North Texas Department of Chemistry LECTURE 14 Dr. Teres D. Golden University of North Texs Deprtment of Chemistry Quntittive Methods A. Quntittive Phse Anlysis Qulittive D phses by comprison with stndrd ptterns. Estimte of proportions of

More information

Problem Set 9. Figure 1: Diagram. This picture is a rough sketch of the 4 parabolas that give us the area that we need to find. The equations are:

Problem Set 9. Figure 1: Diagram. This picture is a rough sketch of the 4 parabolas that give us the area that we need to find. The equations are: (x + y ) = y + (x + y ) = x + Problem Set 9 Discussion: Nov., Nov. 8, Nov. (on probbility nd binomil coefficients) The nme fter the problem is the designted writer of the solution of tht problem. (No one

More information

Non-Linear & Logistic Regression

Non-Linear & Logistic Regression Non-Liner & Logistic Regression If the sttistics re boring, then you've got the wrong numbers. Edwrd R. Tufte (Sttistics Professor, Yle University) Regression Anlyses When do we use these? PART 1: find

More information

Line and Surface Integrals: An Intuitive Understanding

Line and Surface Integrals: An Intuitive Understanding Line nd Surfce Integrls: An Intuitive Understnding Joseph Breen Introduction Multivrible clculus is ll bout bstrcting the ides of differentition nd integrtion from the fmilir single vrible cse to tht of

More information

Section 6.1 Definite Integral

Section 6.1 Definite Integral Section 6.1 Definite Integrl Suppose we wnt to find the re of region tht is not so nicely shped. For exmple, consider the function shown elow. The re elow the curve nd ove the x xis cnnot e determined

More information