A PROBABILISTIC VIEW ON THE ECONOMICS OF GAMBLING
|
|
- Amanda Carpenter
- 8 years ago
- Views:
Transcription
1 A PROBABILISTIC VIEW ON THE ECONOMICS OF GAMBLING MATTHEW ACTIPES Abstract. This paper begis by defiig a probability space ad establishig probability fuctios i this space over discrete radom variables. We the defie the expectatio ad variace of a radom variable with the ultimate goal of provig the Weak Law of Large Numbers. We the apply these ideas to the cocept of gamblig to show why casios exist ad whe it is beeficial to a idividual to gamble as defied by idividual utility. Cotets 1. The Probability Space 1 2. Radom Variables ad Probability Fuctios 2 3. Expectatio ad Variace 4 4. Casio Ecoomics 8 5. Gambler s Utility 9 Ackowledgmets 10 Refereces The Probability Space To motivate the probability space we ll begi by statig that a radom experimet is oe whose results may vary betwee trials eve uder idetical coditios. To try to predict how ofte each trial will produce a certai result we eed to look at the set Ω of all possible outcomes of the radom experimet. We the deote A as a subset of the power set of Ω, which is the collectio of subsets of Ω. Fially, we eed a probability measure P that measures the likelihood of each evet i A occurrig as a result of a radom experimet. With these ideas i mid, we ca defie the otio of a probability space. Defiitio 1.1. A Probability Space is a triple <Ω,A,P> represetig the probability of every possible evet i a system occurrig, where Ω is the set of all possible outcomes (the sample space), A is a collectio of subsets of Ω (evets) which satisfies: A, for a evet a i A, (Ω\a i ) A, if a i A for i=1,2,... the i a i A, Date: August 24,
2 2 MATTHEW ACTIPES ad P is the probability measure where P : A [0, 1] is a fuctio ( satisfyig P(Ω) = ) 1 ad for a coutable set of disjoit evets a 1, a 2...a, P a i = P(a i ). i=1 i=1 Because it is clear each evet a i is a subset of A, we ca exted the otio of set theory to the evets. By itroducig the biary operatios of uio ad itersectio, for ay two evets a j ad a k, we say that a j a k is the evet either a j or a k or both, a j a k is the evet both a j ad a k. Note that if the evets a j ad a k are mutually exclusive, the a j a k =, meaig that both evets caot occur simultaeously. Lookig further at these coditios, if the probability that a j a k occurs depeds oly o the probability that a j occurs disjoit from the probability that a k occurs, the two evets are said to be idepedet. Defiitio 1.2. Two evets are idepedet if ad oly if P(a j a k ) = P(a j )P(a k ). We would ow like to exted the probability measure to our sample space to motivate the probability mass fuctio ad distributio fuctio. First, we will eed to defie the radom variable. 2. Radom Variables ad Probability Fuctios We are ow goig to take the sample space ad assig a umber to each outcome w Ω where each umber cotais some iformatio about the outcome. This fuctio X is called a radom variable. Defiitio 2.1. A radom variable X is a fuctio X: Ω R. Example 2.2. Suppose we toss a coi three times. The Ω = {HHH, HHT, HTH, HTT, THH, TTH, THT, TTT}, where H represets the coi ladig o heads ad T represets the coi ladig o tails. Let s defie our radom variable X to be the fuctio that seds each evet cotaiig a eve umber of heads to 1 ad each evet cotaiig a odd umber of heads to 0. Thus a table of our fuctio looks like: Table 1-1 Outcome HHH HHT HTH HTT THH TTH THT TTT X Defiitio 2.3. A discrete radom variable is a radom variable that takes o a fiite or coutably ifiite umber of values. Remark 2.4. For a discrete radom variable X, we ca arrage the possible values that X takes o as x 1, x 2... where x i x j for i>j. Each of these values of X will have a probability associated with it, P(X = x i ), where 1 i for distict values of X ad [1, ). From this we ca defie the probability mass fuctio ad distributio fuctio of X. Defiitio 2.5. For a discrete radom variable X, f(x i ) is the probability mass fuctio of X if f(x i ) = P(X = x i ) for all i.
3 A PROBABILISTIC VIEW ON THE ECONOMICS OF GAMBLING 3 Defiitio 2.6. The distributio fuctio F(x) for a discrete radom variable X is defied as F (x) = P(X x) = y x f(y), where x R. Note: For ay y x j, where x j is a value take o by X, P(y) = 0. Therefore, for discrete radom variables, f(y) is equivalet to writig y x f(x j ). x j x Note: Aside from discrete radom variables, there are also cotiuous radom variables which do ot satisfy Defiitio 2.3. Defiitio 2.7. A radom variable X is cotiuous if its distributio fuctio ca be writte as F (x) = P (X x) = x where f X is the probability desity fuctio of X. f X (u)du, Remark 2.8. The probability desity fuctio is the cotiuous radom variable s aalog to the discrete radom variable s probability mass fuctio. While this defiitio is importat i the field of probability, this paper will oly focus o discrete radom variables. The previous ideas ca be exteded to the case with two or more discrete radom variables. Oce we defie the joit distributio for the discrete radom variables X ad Y, geeralizatios ca be made to the case with more tha two radom variables. Defiitio 2.9. For two discrete radom variables X ad Y, f(x i, y i ) is the joit probability mass fuctio of X ad Y if for all i. f(x i, y i ) = P(X = x i, Y = y i ) Defiitio For two discrete radom variables X ad Y, the joit distributio fuctio of X ad Y is defied by F (x, y) = P(X x, Y y) = f(x i, y i ), x i x y i y where x i are the values of X less tha or equal to x ad y i are the values of Y less tha or equal to y. Just as with two distict evets, the cocept of idepedece also applies to radom variables. If each pair of evets take from the radom variables X ad Y is idepedet, where oe evet is take from X ad oe from Y, we say that X ad Y are idepedet.
4 4 MATTHEW ACTIPES Defiitio If for all x i ad y i the evets {X = x i } ad {Y = y i } are idepedet, we say that X ad Y are idepedet radom variables. The which meas P(X = x i, Y = y i ) = P(X = x i )P(Y = y i ), f(x i, y i ) = f X (x i )f Y (y i ), where f X ad f Y are the probability mass fuctios of X ad Y. 3. Expectatio ad Variace Now that we are able to calculate the probability that a certai value of X will result from a trial, we wat to kow beforehad what the expected outcome of the trial will be. For a discrete radom variable X we ca write the possible values of X as x 1, x 2,...x ad associate with each the probability P(X = x i ) to fid the expectatio of X. Defiitio 3.1. For a discrete radom variable X the Expectatio of X, deoted E(X), is defied by E(X) = x 1 P(X = x 1 ) x P(X = x ) = x i Ω x i P(X = x i ) = x i Ω x i f(x i ), for [1, ). Note: The expectatio of X is also called the expected value of X or the mea of X ad is ofte deoted by µ. Example 3.2. Suppose that a fair coi is about to be flipped. If it lads o heads, you wi 30 dollars. If it lads o tails, you lose 20 dollars. We ca defie X to be the radom variable that seds the result of the flip to the amout of moey that you wi. The for heads, x 1 is +30 ad f(x 1 ) is For tails, x 2 is -20 ad f(x 2 ) is Thus the expectatio is E(X) = (+30)(0.50) + ( 20)(0.50) = +5. Therefore, o each trial of this game, you are expected to ear 5 dollars i profit. Remark 3.3. The cocept of expectatio is of extreme importace to mathematical gamblig theory. We will see later that expectatio is closely related to et profit whe playig a large umber of games i a casio. For two radom variables X ad Y it ca be show that the expectatio of the sum or product of the variables is equal to the sum or product of the expectatios uder certai coditios. The followig two theorems will oly be proved i the discrete case. Geeralizatios ca easily be made i the case that X ad Y are cotiuous variables, but these geeralizatios are ot ecessary to this paper. Theorem 3.4. If X ad Y are radom variables, the E(X + Y ) = E(X) + E(Y ).
5 A PROBABILISTIC VIEW ON THE ECONOMICS OF GAMBLING 5 Proof. Let f(x,y) be the joit probability mass fuctio of X ad Y. The we have E(X + Y ) = (x + y)f(x, y) x y = xf(x, y) + yf(x, y) x y x y = E(X) + E(Y ). Theorem 3.5. If X ad Y are idepedet radom variables, the E(XY ) = E(X)E(Y ). Proof. Let f(x,y) be the joit probability mass fuctio of X ad Y. X ad Y are idepedet, so f(x,y)=f 1 (x)f 2 (y). Therefore, E(XY ) = xyf(x, y) = xyf 1 (x)f 2 (y) x y x y = [xf 1 (x) yf 2 (y)] x y = x [xf 1 (x)e(y)] = E(X)E(Y ). Also oteworthy is the fact that due to the ature of sums, a costat c ca be pulled through the expectatio operator. Theorem 3.6. Let X be a radom variable. If c is ay costat, the Proof. E(cX) = ce(x). E(cX) = x i Ω cx i f(x i ) = c x i Ω x i f(x i ) = ce(x). Now that we have begu workig with the expectatio of a radom variable X, we d like to have a idea of how widely the values of X are spread aroud E(X). Recallig that E(X) is deoted by µ, we ca defie the cocepts of variace ad stadard deviatio. Defiitio 3.7. For a radom variable X with mea µ the variace of X, deoted Var(X), is defied by Var(X) = E[(X µ) 2 ].
6 6 MATTHEW ACTIPES Defiitio 3.8. The stadard deviatio of X is defied as the positive square root of the variace of X ad is give by σ = Var(X) = E[(X µ) 2 ]. Remark 3.9. For a discrete radom variable X with probability mass fuctio f(x), we ca write the variace of X as Var(X) = E[(X µ) 2 ] = σ 2 = x i Ω(x i µ) 2 f(x i ). With the previous defiitios established, we are ow able to prove a few theorems ecessary i provig the Law of Large Numbers, which is essetial to our justificatio of usig the expected value of a radom variable i determiig logterm ecoomic success of a casio. Theorem For ay costat c, Var(cX) = c 2 Var(X). Proof. Let E(X)=µ ad E(cX)=cµ. The Var(cX) = E[(cX cµ) 2 ] = E[c 2 (X µ) 2 ] = c 2 Var(X). Theorem If X ad Y are idepedet radom variables, the Var(X + Y ) = Var(X) + Var(Y ) = Var(X Y ). Proof. Let µ X be the expectatio of X ad µ Y be the expectatio of Y. The we have Var(X + Y ) = E[[(X + Y ) (µ X + µ Y )] 2 ] = E[[(X µ X ) + (Y µ Y )] 2 ] = E[(X µ X ) 2 + 2(X µ X )(Y µ Y ) + (Y µ Y ) 2 ] = E[(X µ X ) 2 ] + 2E[(X µ X )(Y µ Y )] + E[(Y µ Y ) 2 ]. From here we otice that because X ad Y are idepedet, (X µ X ) ad (Y µ Y ) are also idepedet. Also, because E(X) is defied as µ X ad E(Y) is defied as µ Y, the E(X µ X ) = E(Y µ Y ) = 0. Therefore we have Var(X + Y ) = E[(X µ X ) 2 ] + 2E[(X µ X )(Y µ Y )] + E[(Y µ Y ) 2 = E[(X µ X ) 2 ] + 2[E(X µ X )E(Y µ Y )] + E[(Y µ Y ) 2 = E[(X µ X ) 2 ] + E[(Y µ Y ) 2 = Var(X) + Var(Y ). To complete this proof, we otice that Var(X Y ) = Var(X) + Var(( 1)Y ). The by Theorem 3.10 this equals Var(X) + ( 1) 2 Var(Y ) = Var(X) + Var(Y ) = Var(X + Y ). Remark Notice that the above proof ca be geeralized to ay umber of idepedet radom variables. Therefore, we ca see that the variace of a sum of idepedet radom variables is equal to the sum of the variaces.
7 A PROBABILISTIC VIEW ON THE ECONOMICS OF GAMBLING 7 Theorem (Chebyshev s Iequality) Suppose X is a radom variable with fiite mea ad variace. The for ay ε>0, P( X µ ε) σ2 ε 2. Proof. Let f(x) be the probability mass fuctio of X. The σ 2 = E[(X µ) 2 ] = x i Ω(x i µ) 2 f(x i ). But if we limit the sum over the x such that x µ ε, the value of the sum ca oly decrease sice we are oly workig with oegative terms. Therefore we have σ 2 (x µ) 2 f(x) ε 2 f(x) = ε 2 f(x) = ε 2 P( x µ ε). x µ ε x µ ε x µ ε Dividig both sides by ε 2 gives the desired result of P( X µ ε) σ2 ε 2. Usig Chebyshev s Iequality, we get a iterestig result whe we plug i ε = kσ, amely that P( X µ kσ) 1 k 2. This result shows that for k 1, the probability that a certai value of X lies outside of the rage (µ kσ, µ + kσ) is bouded above by 1 k 2 for ay fiite µ ad σ. Example Let k=3. The usig Chebyshev s Iequality, P( X µ 3σ) This meas that the probability of ay value of X beig withi three stadard deviatios of the mea is at least about 89%. Cosequetly, there is at most about a 11% chace that the value lies 3σ away from µ. Remark Chebyshev s Iequality provides oly a geerous upper boud o the probability that a radom variable lies withi a certai umber of stadard deviatios. For may distributios, the probability is much smaller. For example, i the ormal distributio there is about a.13% chace that the radom variable lies three stadard deviatios from the mea. We are ow i positio to state oe of the most importat theorems i casio ecoomics. Usig the cocept of variace ad Chebyshev s Iequality, we ca prove the Weak Law of Large Numbers, showig that the expected value of a casio game is directly related to the game s log-term profit. Theorem Weak Law of Large Numbers Let X 1, X 2,, X be mutually idepedet radom variables, each with fiite mea ad variace. The for ay ε>0, if S = X 1 + X X, the lim P ( S µ ε ) = 0.
8 8 MATTHEW ACTIPES Proof. By Theorem 3.4, we have ( ) ( ) S X1 + X X E = E ad by a extesio of Theorem 3.11, = 1 [E(X 1)+E(X 2 )+ +E(X )] = 1 (µ) = µ, Var(S ) = Var(X 1 + X X ) = Var(X 1 ) + Var(X 2 ) + + Var(X ) = σ 2, so by Theorem 3.10, we have that Var ( S ) = 1 2 Var(S ) = σ2. Now we ca ivoke Chebyshev s Iequality. By lettig X = S, we have ( P S ) µ ε σ2 ε 2. Now we eed oly take the limit as approaches ifiity of both sides to get ( lim P S ) µ ε = 0. This theorem states that as the umber of trials approaches ifiity, the probability that the average of the results deviates from the expected value approaches zero. A good example is that of a large umber of coi flips. While the disparity betwee heads ad tails may be large for a small sample space, as more ad more flips occur, the total amout of heads will ed up very close to the total amout of tails. This idea is essetial to the busiess model of a casio. From this theorem, we ca deduce why casios choose to ope ad why they stay i busiess. 4. Casio Ecoomics Busiess firms work i the log ru. That is, they are willig to sped moey or forgo profits ow to esure a higher profit i the big picture of their compay. Casios are o differet. They are willig to face days where they make a egative profit as log as they ed up i the positive at the ed of some larger amout of time. Therefore, casio operators eed to esure that they actually will see a positive et icome if they stay i busiess log eough. Because of this, they rely heavily o the Law of Large Numbers. Let s look at oe of the simplest games a casio ca offer: roulette. A roulette wheel cosists of 38 evely spaced pockets umbered 0, 00, 1, 2,, 36, where 0 ad 00 are colored gree, ad the other 36 umbers are split ito two groups of eightee, with oe group colored black ad the other colored red. The wheel is spu ad a ball is dropped ito it while bets are placed o where the ball will lad. Whe played accordig to desig, this game relies completely o the laws of probability. Therefore, it makes sese to quote the expected value of the bets o the table. Example 4.1. The payout for bettig o red is 1 to 1. Sice there are 38 pockets o the wheel, 18 of them beig red, we otice that the odds of wiig this bet are
9 18 38 A PROBABILISTIC VIEW ON THE ECONOMICS OF GAMBLING Therefore the odds of losig the bet are 38. We the see that E(Red) = ( ) ( ) a i p i = (1) + ( 1) = This says that for every oe dollar bet we place o red, we are expected to lose about 5.26 cets, or the casio makes about 5.26 cets. This calculatio ca be carried out for all of the bets to show that every bet has a egative expected value. Furthermore, this holds true for every chace-based game offered by the casio. Recall that the Weak Law of Large Numbers states for ay ε>0, ( lim P S ) µ ε = 0. Therefore as the casio remais i busiess loger ad the umber of bets customers place icreases, the probability that the casio does ot ear their expected value approaches zero. Seeig as the expected value of the casio is positive, the casio ca expect with probabilistic certaity that they will make a positive et profit. I other words, the house always wis. 5. Gambler s Utility So if the house is always expected to come out o top i the log ru, why do people gamble? To aswer this we eed to defie a gambler s utility fuctio. Let s deote the utility fuctio of a gambler as U(x i ). We ca work uder the assumptio that all ratioal people would prefer, uder idetical circumstaces, to have more moey tha ot. Therefore, U(x i ) is icreasig. For every evet x i there is a associated utility U(x i ) that will add or subtract from the gambler s total utility, depedig o the outcome. Therefore each gamble has with it a associated expected utility. Defiitio 5.1. For a discrete radom variable X, let P(X = x i ) = f(x i ). The the Expected Utility of X, deoted ϕ(x), is defied as ϕ(x) = x i Ω U(x i )f(x i ). Remark 5.2. We otice that a gambler will accept a gamble if ϕ(x)>0 ad will declie a gamble if ϕ(x)<0. I the case that ϕ(x) = 0, the gambler is idifferet as to whether or ot he accepts the gamble. Let s ow go back to our roulette example. The gambler who plays roulette is willig to do so eve though he has a egative expected retur. This shows that for him, ϕ(x) 0, which implies that he receives some amout of positive utility from the act of gamblig itself. This ca be likeed to thikig of gamblig as a form of etertaimet, such as goig to a baseball game. The expected retur of goig to a baseball game is egative sice there is a cost for tickets, parkig etc., but there is also positive utility gaied from the experiece. Sice the gambler was willig to accept the bet with a egative expected retur, he would surely opt to take part i a similar gamble with a expected retur of 0. Because of this, we say that he is risk lovig.
10 10 MATTHEW ACTIPES Defiitio 5.3. A Risk Lovig perso will always accept a fair gamble. I other words, give the choice of earig the same amout of moey through gamble or through certaity, the risk lovig perso will opt for the gamble. Remark 5.4. From this defiitio we ca see that a risk lovig perso has icreasig margial utility. This meas that the perso derives more utility from wiig a certai amout of moey tha from losig that same amout. Therefore, his margial utility is icreasig ad cocave up. Returig to the previous defiitio, we kow that for ay positive amout of moey c, U(x i ) U(x i c) U(x i + c) U(x i ). But if the expected retur of a gamble is ot 0, such as i roulette, we ca defie the utility that a perso receives from gamblig. Defiitio 5.5. The Utility of Risk, deoted by U R, is defied as U R = max[(b c)] for b,c satisfyig U(x i ) U(x i b) U(x i + c) U(x i ). Usig this equatio we ca determie for each idividual how much utility they derive from the experiece of gamblig. Example 5.6. We ve quoted the expected value of a roulette bet to be about cets per dollar. We kow that someoe who makes this bet has a expected utility greater tha or equal to zero, so usig expected value we ca say U(x i ) U(x i 1) U(x i ) U(x i ). Thus, this perso has a utility of risk greater tha or equal to Therefore while gamblig agaist the house seems illogical mathematically, the cocept of utility ca be applied to show that gamblig is ideed logical. Furthermore, usig a idividual s utility of risk ad their utility fuctio we ca calculate the maximum expected loss a bet ca carry that will still ot lower the idividual s expected utility. The combiatio of probability ad ecoomics ca therefore justify the cocept of gamblig both for the casio ad for the gambler. Ackowledgmets. I d like to thak my metor Ya Zhag for his help throughout this process. His guidace ad support has bee essetial to my work ad I appreciate all he s doe for me. I d also like to thak Peter May for ivitig me to participate i this program ad to everyoe who helped orgaize ad ru the 2012 REU. Refereces [1] Murray R. Spiegel Probability ad Statistics McGraw-Hill Ic [2] Vai K. Borooah Risk ad Ucertaity Uiversity of Ulster [3] James H. Stapleto Models for Probability ad Statistical Iferece: Theory ad Applicatios Joh Wiley ad Sos 2008
Chapter 6: Variance, the law of large numbers and the Monte-Carlo method
Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value
More informationMARTINGALES AND A BASIC APPLICATION
MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measure-theoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this
More informationDiscrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13
EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may
More informationWeek 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable
Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5
More informationI. Chi-squared Distributions
1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.
More informationCase Study. Normal and t Distributions. Density Plot. Normal Distributions
Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca
More informationA Mathematical Perspective on Gambling
A Mathematical Perspective o Gamblig Molly Maxwell Abstract. This paper presets some basic topics i probability ad statistics, icludig sample spaces, probabilistic evets, expectatios, the biomial ad ormal
More informationAsymptotic Growth of Functions
CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll
More informationOverview of some probability distributions.
Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability
More informationProperties of MLE: consistency, asymptotic normality. Fisher information.
Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout
More informationChapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:
Chapter 7 - Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries
More informationTHE ABRACADABRA PROBLEM
THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected
More informationThe following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles
The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio
More information5: Introduction to Estimation
5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample
More informationInfinite Sequences and Series
CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...
More informationHypothesis testing. Null and alternative hypotheses
Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate
More informationIn nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008
I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces
More information3 Basic Definitions of Probability Theory
3 Basic Defiitios of Probability Theory 3defprob.tex: Feb 10, 2003 Classical probability Frequecy probability axiomatic probability Historical developemet: Classical Frequecy Axiomatic The Axiomatic defiitio
More informationDetermining the sample size
Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors
More informationMath C067 Sampling Distributions
Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters
More informationIncremental calculation of weighted mean and variance
Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically
More informationUniversity of California, Los Angeles Department of Statistics. Distributions related to the normal distribution
Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chi-square (χ ) distributio.
More informationCenter, Spread, and Shape in Inference: Claims, Caveats, and Insights
Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the
More informationConfidence Intervals for One Mean
Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a
More informationChapter 7 Methods of Finding Estimators
Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of
More information5 Boolean Decision Trees (February 11)
5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected
More informationHypergeometric Distributions
7.4 Hypergeometric Distributios Whe choosig the startig lie-up for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you
More informationTHE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n
We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample
More informationMeasures of Spread and Boxplots Discrete Math, Section 9.4
Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,
More informationPROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM
PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics
More informationLecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem
Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits
More informationUC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006
Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam
More informationSAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx
SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval
More informationChapter 14 Nonparametric Statistics
Chapter 14 Noparametric Statistics A.K.A. distributio-free statistics! Does ot deped o the populatio fittig ay particular type of distributio (e.g, ormal). Sice these methods make fewer assumptios, they
More information1 Computing the Standard Deviation of Sample Means
Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.
More information15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011
15.075 Exam 3 Istructor: Cythia Rudi TA: Dimitrios Bisias November 22, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 A compay makes high-defiitio
More informationZ-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown
Z-TEST / Z-STATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses about
More informationLecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)
18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the
More informationIrreducible polynomials with consecutive zero coefficients
Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem
More informationConvexity, Inequalities, and Norms
Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for
More informationNon-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring
No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy
More information1. C. The formula for the confidence interval for a population mean is: x t, which was
s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value
More informationNATIONAL SENIOR CERTIFICATE GRADE 12
NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS
More informationGCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number.
GCSE STATISTICS You should kow: 1) How to draw a frequecy diagram: e.g. NUMBER TALLY FREQUENCY 1 3 5 ) How to draw a bar chart, a pictogram, ad a pie chart. 3) How to use averages: a) Mea - add up all
More informationBetting on Football Pools
Bettig o Football Pools by Edward A. Beder I a pool, oe tries to guess the wiers i a set of games. For example, oe may have te matches this weeked ad oe bets o who the wiers will be. We ve put wiers i
More informationDepartment of Computer Science, University of Otago
Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly
More informationNormal Distribution.
Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued
More informationBaan Service Master Data Management
Baa Service Master Data Maagemet Module Procedure UP069A US Documetiformatio Documet Documet code : UP069A US Documet group : User Documetatio Documet title : Master Data Maagemet Applicatio/Package :
More informationThe Stable Marriage Problem
The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,
More informationPENSION ANNUITY. Policy Conditions Document reference: PPAS1(7) This is an important document. Please keep it in a safe place.
PENSION ANNUITY Policy Coditios Documet referece: PPAS1(7) This is a importat documet. Please keep it i a safe place. Pesio Auity Policy Coditios Welcome to LV=, ad thak you for choosig our Pesio Auity.
More informationHow To Solve The Homewor Problem Beautifully
Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log
More informationPSYCHOLOGICAL STATISTICS
UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION B Sc. Cousellig Psychology (0 Adm.) IV SEMESTER COMPLEMENTARY COURSE PSYCHOLOGICAL STATISTICS QUESTION BANK. Iferetial statistics is the brach of statistics
More informationCHAPTER 7: Central Limit Theorem: CLT for Averages (Means)
CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:
More information.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth
Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,
More informationYour organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:
Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network
More informationPre-Suit Collection Strategies
Pre-Suit Collectio Strategies Writte by Charles PT Phoeix How to Decide Whether to Pursue Collectio Calculatig the Value of Collectio As with ay busiess litigatio, all factors associated with the process
More informationCHAPTER 3 THE TIME VALUE OF MONEY
CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all
More informationSoving Recurrence Relations
Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree
More informationA probabilistic proof of a binomial identity
A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two
More informationSequences and Series
CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their
More informationBuilding Blocks Problem Related to Harmonic Series
TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite
More information0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5
Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.
More informationI. Why is there a time value to money (TVM)?
Itroductio to the Time Value of Moey Lecture Outlie I. Why is there the cocept of time value? II. Sigle cash flows over multiple periods III. Groups of cash flows IV. Warigs o doig time value calculatios
More informationNOTES ON PROBABILITY Greg Lawler Last Updated: March 21, 2016
NOTES ON PROBBILITY Greg Lawler Last Updated: March 21, 2016 Overview This is a itroductio to the mathematical foudatios of probability theory. It is iteded as a supplemet or follow-up to a graduate course
More informationSampling Distribution And Central Limit Theorem
() Samplig Distributio & Cetral Limit Samplig Distributio Ad Cetral Limit Samplig distributio of the sample mea If we sample a umber of samples (say k samples where k is very large umber) each of size,
More informationThe analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection
The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity
More information5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?
5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso
More informationExample 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).
BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly
More informationElementary Theory of Russian Roulette
Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some
More informationHere are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.
This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio
More informationResearch Article Sign Data Derivative Recovery
Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 63070, 7 pages doi:0.540/0/63070 Research Article Sig Data Derivative Recovery L. M. Housto, G. A. Glass, ad A. D. Dymikov
More informationDefinition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean
1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.
More informationPresent Values, Investment Returns and Discount Rates
Preset Values, Ivestmet Returs ad Discout Rates Dimitry Midli, ASA, MAAA, PhD Presidet CDI Advisors LLC dmidli@cdiadvisors.com May 2, 203 Copyright 20, CDI Advisors LLC The cocept of preset value lies
More informationChapter 5: Inner Product Spaces
Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples
More informationOutput Analysis (2, Chapters 10 &11 Law)
B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should
More informationCS103X: Discrete Structures Homework 4 Solutions
CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least
More informationFI A CIAL MATHEMATICS
CHAPTER 7 FI A CIAL MATHEMATICS Page Cotets 7.1 Compoud Value 117 7.2 Compoud Value of a Auity 118 7.3 Sikig Fuds 119 7.4 Preset Value 122 7.5 Preset Value of a Auity 122 7.6 Term Loas ad Amortizatio 123
More informationFM4 CREDIT AND BORROWING
FM4 CREDIT AND BORROWING Whe you purchase big ticket items such as cars, boats, televisios ad the like, retailers ad fiacial istitutios have various terms ad coditios that are implemeted for the cosumer
More informationLECTURE 13: Cross-validation
LECTURE 3: Cross-validatio Resampli methods Cross Validatio Bootstrap Bias ad variace estimatio with the Bootstrap Three-way data partitioi Itroductio to Patter Aalysis Ricardo Gutierrez-Osua Texas A&M
More informationTHE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction
THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,
More informationMaximum Likelihood Estimators.
Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio
More informationStatistical inference: example 1. Inferential Statistics
Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either
More information1 Correlation and Regression Analysis
1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio
More informationINVESTMENT PERFORMANCE COUNCIL (IPC)
INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks
More informationHow to use what you OWN to reduce what you OWE
How to use what you OWN to reduce what you OWE Maulife Oe A Overview Most Caadias maage their fiaces by doig two thigs: 1. Depositig their icome ad other short-term assets ito chequig ad savigs accouts.
More informationResearch Method (I) --Knowledge on Sampling (Simple Random Sampling)
Research Method (I) --Kowledge o Samplig (Simple Radom Samplig) 1. Itroductio to samplig 1.1 Defiitio of samplig Samplig ca be defied as selectig part of the elemets i a populatio. It results i the fact
More informationChapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity
More informationFactoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>
(March 16, 004) Factorig x 1: cyclotomic ad Aurifeuillia polyomials Paul Garrett Polyomials of the form x 1, x 3 1, x 4 1 have at least oe systematic factorizatio x 1 = (x 1)(x 1
More informationCentral Limit Theorem and Its Applications to Baseball
Cetral Limit Theorem ad Its Applicatios to Baseball by Nicole Aderso A project submitted to the Departmet of Mathematical Scieces i coformity with the requiremets for Math 4301 (Hoours Semiar) Lakehead
More informationSECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES
SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,
More informationTradigms of Astundithi and Toyota
Tradig the radomess - Desigig a optimal tradig strategy uder a drifted radom walk price model Yuao Wu Math 20 Project Paper Professor Zachary Hamaker Abstract: I this paper the author iteds to explore
More informationInformation about Bankruptcy
Iformatio about Bakruptcy Isolvecy Service of Irelad Seirbhís Dócmhaieachta a héirea Isolvecy Service of Irelad Seirbhís Dócmhaieachta a héirea What is the? The Isolvecy Service of Irelad () is a idepedet
More informationEstimating Probability Distributions by Observing Betting Practices
5th Iteratioal Symposium o Imprecise Probability: Theories ad Applicatios, Prague, Czech Republic, 007 Estimatig Probability Distributios by Observig Bettig Practices Dr C Lych Natioal Uiversity of Irelad,
More informationBasic Elements of Arithmetic Sequences and Series
MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic
More informationApproximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find
1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.
More informationProject Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments
Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 6-12 pages of text (ca be loger with appedix) 6-12 figures (please
More informationTrigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is
0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values
More informationBENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets
BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts
More informationEntropy of bi-capacities
Etropy of bi-capacities Iva Kojadiovic LINA CNRS FRE 2729 Site école polytechique de l uiv. de Nates Rue Christia Pauc 44306 Nates, Frace iva.kojadiovic@uiv-ates.fr Jea-Luc Marichal Applied Mathematics
More informationConfidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.
Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).
More information