Notes on exponential generating functions and structures.

Size: px
Start display at page:

Download "Notes on exponential generating functions and structures."

Transcription

1 Notes o expoetial geeratig fuctios ad structures. 1. The cocept of a structure. Cosider the followig coutig problems: (1) to fid for each the umber of partitios of a -elemet set, (2) to fid for each the umber of permutatios of a -elemet set, (3) to fid for each the umber of subsets of a -elemet set, (4) to fid for each the umber of labelled trees o a set of vertices. The precedig problems are of a differet ature tha those we typically solve usig ordiary geeratig fuctios. The differece is that the thigs to be couted are tied to a specified - elemet set for each. For istace, if we wish to list all partitios of a 5-elemet set, we ca do it oly by itroducig a particular 5-elemet set to work with. Thus we might use the set {a, b, c, d, e} ad begi listig the partitios {{a, b}, {c, d, e}}, {{c}, {a, b, d, e}}, ad so o. O the other had if we wish to list all partitios of the umber 5, there is o uderlyig set of 5 elemets to be itroduced; we just list the possibilities: (5), (4, 1) ad so o. What is commo to the problems listed at the outset is that i each of them, we begi for each with a set of elemets, ad the we are asked to cout all possible ways of imposig a certai structure upo that give set. Thus for a partitio, we are to structure the set by arragig its elemets ito blocks. For a permutatio, we are to arrage them i some order. For a subset, we are to arrage them ito those chose to belog to the subset, ad those ot. For a tree, we are to arrage the elemets ito the vertices of a tree. The purpose of expoetial geeratig fuctios is to solve coutig problems like these, i which some kid of structure is specified ad we are asked to fid for each the umber of ways to arrage the elemets of a -elemet set ito such a structure. We defie the expoetial geeratig fuctio associated with a coutig problem for structures to be F (x) = f() x!, where f() is the umber of structures o a -elemet set. For the momet, we will have to accept this o faith as a good defiitio. As we proceed further, however, this defiitio will be justified by the fact that it leads to additio ad multiplicatio priciples that are appropriate to structure-coutig problems. As we shall see, it also leads to importat ew coutig priciples that go beyod what we ca do with additio ad multiplicatio. 2. Trivial structures Before it is possible to work out ay sigificat examples of expoetial geeratig fuctios it will be ecessary to build a small repetoire of geeratig fuctios for some seemigly stupid structures. We call them trivial structures because the structures themselves have little cotet. Nevertheless we will fid that their geeratig fuctios are ot i ay sese trivial, but may be iterestig fuctios. Furthermore these structures are ot at all trivial i their usefuless, for every structure we cosider will have these as buildig blocks at some level. We begi with the trivial structure, which is the structure of beig a set. What we mea is that to impose the trivial structure o a give set is to give the elemets oly that structure which they already have: the structure of a set. The poit is, there is just oe such structure o every set. Thus the umber of trivial beig a set structures o a -elemet set is f() = 1 for every. The expoetial geeratig fuctio for this trivial structure is therefore F (x) = =0 1 x! = ex.

2 Note that the geeratig fuctio for this most trivial structure is ot at all a trivial fuctio. More geerally, we ca cosider trivial structures that express restrictios o the size of a set. The simplest such restrictio is the structure of beig a 1-elemet set. To impose this structure o a 1-elemet set is to give it oly the structure it already has but to impose it o aythig but a oe elemet set is impossible. I short, there is by defiitio exactly oe structure of 1- elemet set o every 1-elemet set, ad oe o ay other set. The umber of trivial 1-elemet set structures o a -elemet set is therefore f() = 1 if = 1, ad f() = 0 otherwise. The correspodig geeratig fuctio is F (x) = f() x! = x. I a similar way, correspodig to ay restrictio we might care to place o the size of a set we ca defie a trivial structure reflectig that restrictio. By defiitio there is to be oe such structure o a set if it obeys the restricitio, ad oe if it does t. The umber f() of our trivial structures o a -elemet set is thus, by defiitio, the idicator fuctio that is 1 for good values of ad 0 for bad values. The expoetial geeratig fuctio F (x) = f()x /! for our trivial structure is the simply the sum of x /! take over all allowed values of. Fortuately, i may cases this is simple to express i closed form, as i the two examples we just did. Here are some examples of trivial structures. Although the first four examples below are quite trivial ideed, they are also the oes that are most ofte useful. (1) The trivial structure of set : F (x) = e x. (2) The trivial 1-elemet set structure: F (x) = x. (3) The trivial empty set strucutre: F (x) = 1. (4) The trivial o-empty set structure: F (x) = e x 1. (5) The trivial eve-size set structure: F (x) = cosh(x) = (e x + e x )/2. (6) The trivial odd-size set structure: F (x) = sih(x) = (e x e x )/2. As a exercise to be sure you uderstad this so far, I suggest you pause here ad work out i each of the above examples why the geeratig fuctio F (x) is what I said it is. 3. Additio ad multiplicatio priciples To cout aythig iterestig usig expoetial geeratig fuctios, we eed to kow what it meas to add ad multiply them. To state the relevat priciples, let s suppose that we have give ames to some kids of structures, say f-structures, g-structures, ad h-structures. We let f() be the umber of f-structures o a -elemet set, so the expoetial geeratig fuctio coutig f-structures is F (x) = f()x /!. Similarly for g ad h. Additio priciple for expoetial geeratig fuctios: Suppose that the set of f-structures o each set is the disjoit uio of the set of g-structures ad the set of h-structures. The F (x) = G(x) + H(x). The additio priciple is rather obvious ad is o differet from the additio priciples for other types of eumeratio, such as straight coutig, or ordiary geeratig fuctios. As a example, we ca cosider f = trivial structure, g = empty structure, h = o-empty structure. Every set is either empty or o-empty, ad these possibilities are mutually exclusive, so we have F (x) = G(x) + H(x). I this case, we kow that F (x) = e x ad G(x) = 1, which yields the formula H(x) = e x 1 for the expoetial geeratig fuctio of the o-empty set structure. The power of expoetial geeratig fuctios begis to appear whe we cosider the multiplicatio priciple. We begi by defiig a otio of product of two structures.

3 Defiitio: Let g ad h deote two types of structures o fiite sets. A g h structure o a set A cosists of (i) a ordered partitio of A ito disjoit subsets A = A 1 A 2, (ii) a g-structure o A 1, ad (iii) a h-structure o A 2, where the structures i (ii) ad (iii) are chose idepedetly. Multiplicatio priciple for expoetial geeratig fuctios: If G(x) ad H(x) are the expoetial geeratig fuctios for g-structures ad h-structures, respectively, the the expoetial geeratig fuctio for g h structures is F (x) = G(x)H(x). There is a atural geeralizatio of this priciple to the product of three or more geeratig fuctios. Namely, a g 1 g 2 g r structure o A cosists of a ordered partitio of A ito r disjoit subsets A = A 1 A 2 A r, ad idepedetly chose g i structures o each subset A i. With this defiitio, to give a g 1 g 2 g r structure is equivalet to givig a g 1 (g 2 g r ) structure, ad it the follows by iductio o r that the geeratig fuctio for g 1 g 2 g r structures is F (x) = G 1 (x)g 2 (x) G r (x). 4. Examples (1) Let us fid the expoetial geeratig fuctio for the umber of subsets of a -elemet set. To choose a subset of A is equivalet to choosig a ordered partitio of A ito A 1 = the subset, ad A 2 = its complemet. No further structure is imposed o A 1 or A 2, which is to say that we have the trivial structure o each of them. We apply the multiplicatio priciple with G(x) ad H(x) both equal to the trivial structure geeratig fuctio e x, obtaiig the geeratig fuctio for subsets as F (x) = e x e x = e 2x = (2x) = 2 x!!. Of course this accords with our prior kowledge that a -elemet set has 2 subsets. (2) Modifyig the previous example a bit, let us fid the geeratig fuctio coutig o-empty subsets of a -elemet set. We still have trivial structure o A 2, but we are ow imposig the structure of o-empty set o A 1. Thus we get F (x) = (e x 1)e x = e 2x e x = (2 1) x!. Agai we get the expected aswer, sice we already kew that there are 2 1 o-empty subsets of a -elemet set. (3) Let us fix a umber k ad cout fuctios from a -elemet set to {1,..., k}. As a structure o the -elemet set A, to give a fuctio A {1,..., k} is equivalet to givig the ordered partitio A = A 1 A k i which A i is the set of elemets of A that are mapped by the fuctio to i. I other words, our structure is equivalet to a product of k trivial structures. Therefore we should multiply together k trivial-structure geeratig fuctios to get F (x) = (e x ) k = e kx = k x! as the geeratig fuctio coutig fuctios A {1,..., k} o a -elemet set A. Oce agai this accords with our prior kowledge that there are k such fuctios.

4 (4) The previous example might seem rather elemetary, but cosider this slight variatio: we will cout surjective fuctios from a -elemet set A to {1,..., k}. The oly differece from the previous example is that ow each block A i i the ordered partitio that describes the fuctio must be o-empty. I other words our structure is equivalet to the product of k trivial oempty set structures. We ca solve this problem as easily as the last oe, replacig e x for the trivial structure with e x 1 for the o-empty set structure. This yields the geeratig fuctio F (x) = (e x 1) k for surjective fuctios A {1,..., k} o a -elemet set A. From our study of distributio problems, we kow that the umber of partitios of a -elemet set A ito k blocks is (by defiitio) the Stirlig umber S(, k). The umber of ordered partitios ito k blocks, or equivaletly of surjectios A {1,... k}, is k!s(, k). Therefore we have the idetity or (e x 1) k = (e x 1) k k! = k!s(, k) x!, S(, k) x!. So we have come up with a expoetial geeratig fuctio for the Stirlig umbers S(, k) as varies, usig oly extremely simple combiatorial cosideratios. By cotrast a ordiary geeratig fuctio for these same umbers ca oly be arrived at through a fairly subtle bijectio. (5) I the last part of example (4) we have foud the expoetial geeratig fuctio coutig uordered partitios of a -elemet set ito k blocks, sice the umber of these is S(, k). Summig over all values of k we ca cout all partitios of a -elemet set. Their expoetial geeratig fuctio is therefore B x! = k (e x 1) k k! = e ex 1 where the Bell umber B is defied to be the umber of all partitios of a -elemet set. Here we ca take ote of a curious fact: the Bell umber geeratig fuctio we just foud turs out to have the form G(H(x)) with G(x) = e x the geeratig fuctio for the trivial structure ad H(x) = e x 1 the geeratig fuctio for the trivial o-empty structure. We shall explai this later o. (6) We ow cout permutatios of a -elemet set. I this example we will idetify permuatios of A with liear orderigs of A, rather tha with bijectios from A to itself. We will see later o what happes if we take the latter poit of view istead. If = 0, so the set is empty, we shall agree that there is a uique empty permutatio. Otherwise, for a oempty set, we ca choose a permutatio recursively as follows: first decide the first elemet of the permutatio, the decide how to permute the remaiig elemets. Note that this is correct for = 1 because of our covetio that the empty set has oe permutatio. We ca describe the choices ivolved icely i terms of product structures ad trivial structures. To choose a first elemet ad a permutatio of the remaiig elemets is to choose a product structure of the form (oe-elemet set) (permutatio). More precisely, such a product structure cosists of a ordered partitio A = A 1 A 2 i which A 1 is required to cosist of a sigle elemet, together with a permutatio of the elemets of A 2. Our chose first elemet becomes the elemet of A 1 here.

5 If F (x) stads for the expoetial geeratig fuctio coutig permutatios we apply the additio ad multiplicatio priciples to get the idetity F (x) = 1 + xf (x). The first term here is the trivial empty set structure coutig the empty permutatio. The term xf (x) couts permutatios of o-empty sets by the multiplicatio priciple, the factor x beig the geeratig fuctio for the trivial oe-elemet set structure. Solvig for F (x), we have F (x) = 1/(1 x) = x =! x!, i agreemet with our prior kowledge that there are! permutatios of a -elemet set. Note that this computatio has othig to do with the repetitio priciple for ordiary geeratig fuctios. We are dealig here with 1/(1 x) as a expoetial geeratig fuctio, ad it does t cout repetitios of aythig. (7) We have ow solved all but oe of the coutig problems metioed at the begiig of these otes. The problem ot yet solved is that of coutig trees o a -elemet set of vertices. There are may possible versios of this problem, some of which we will cosider i detail later o. For ow, we solve oly oe versio of the problem. We will cout biary trees whose vertices, icludig the root, are labelled by the give -elemet set. We shall agree that the empty tree couts as a biary tree. To choose a biary tree o a o-empty vertex set A, we partitio A ito three parts: the root A 1, the left subtree A 2, ad the right subtree A 3. Sice there is oly oe root vertex, the structure o A 1 is the trivial 1-elemet set structure. The other parts A 2 ad A 3 are agai give the structure of biary tree. Note that our agreemet that the empty tree couts as a tree allows for the possibility that A 2 ad/or A 3 are empty. Let us deote the expoetial geeratig fuctio for labelled biary trees by T (x). The the descriptio we have just give leads to the idetity Solvig for T (x), we get T (x) = 1 + xt (x) 2. T (x) = 1 1 4x = C x = x!c 2x!, Where C is the -th Catala umber. At first it might appear that this example is essetially the same as our earlier determiatio of the umber of biary trees o vertices, usig ordiary geeratig futios. I fact, however, there is a profoud differece. What we couted here was ot abstract ulabelled biary trees, but labelled biary tree structures o a give set of vertices. Ideed, we do ot fid that the umber of them is C, but rather!c, sice T (x) is a expoetial geeratig fuctio. The cofusio results from the accidetal fact that the expoetial geeratig fuctio for!c ad the ordiary geeratig fuctio for C are the same. This accidet occurred because biary trees have a iheret order, so there are exactly! ways to label a ulabelled biary tree o verticies. Later, we will use expoetial geeratig fuctios to cout uordered trees, ad the this accidetal pheomeo will ot occur. 5. The fuctioal compositio priciple We are ow goig to discover the real power of expoetial geeratig fuctios. This power comes from the fact that if G(x) ad H(x) are expoetial geeratig fuctios, the the fuctioal compositio G(H(x)) has a combiatorial iterpretatio.

6 I order for G(H(x)) to make sese as a formal power series, H(x) must satisfy H(0) = 0, that is, the costat term of the geeratig fuctio H(x) must be zero. Combiatorially, this meas that H(x) must cout structures that oly exist o o-empty sets, sice h(0), the umber of h-structures o the empty set, is required to be zero. Defiitio: Let g ad h be types of structures, ad assume that there are o h-structures o the empty set. A composite g h structure o a set A cosists of (i) a partitio of A ito ay umber of blocks, (ii) idepedetly chose h-structures o each block of the partitio, ad (iii) a g-structure o the set of blocks. Here the structure i (iii) is also chose idepedetly from the structures i (ii). Compositio priciple: If the expoetial geeratig fuctios for g-structures ad h-structures are G(x) ad H(x), respectively, where H(0) = 0, the the expoetial geeratig fuctio for composite g h structures is give by F (x) = G(H(x)). There is a importat poit to be made about the way i which the partitio i a composite structure is chose. A priori, the partitio should be chose freely, without ay restrictio o its umber of blocks or o their sizes. However, we ca always impose implicit restrictios o these by usig suitable h- ad g-structures i parts (ii) ad (iii) of the defiitio of composite structure. I particular, we ca ofte impose useful restrictios by takig trivial structures for h or g. A secod poit relates to the requiremet that H(0) = 0. Notice that i formig a composite structure, you choose h-structures o the blocks, which are o-empty by defiitio. I the defiitio of composite structure it therefore makes o differece how may h-structures there are o the empty set. If we wat to use for h a type of structure which does ot have H(0) = 0, we ca always compesate by subtractig the costat term of H(x) from H(x), modifyig it to obtai a ew series Ĥ(x) with Ĥ(0) = 0. This should oly be doe with care, however. I most cases, whe we have aalyzed the problem correctly, the H(x) that goes ito a applicatio of the compositio priciple will aturally have H(0) = 0. If it does ot, it may be a sig of error i our logic. 6. Compositio priciple examples (1) Cosider agai the Bell umber geeratig fuctio e ex 1 which couts all partitios of a -elemet set. Before, we had to get this by summig the geeratig fuctio for Stirlig umbers S(, k) over all values of k. Usig the compositio priciple, we ca get the result directly. The structure of a partitio of A is merely a composite structure i which the structures o each block ad o the set of blocks are all trivial. However, ote that the structure take o each block is required to be oe which oly exists o o-empty sets. Therefore we should take g to be the trivial structure ad h to be the o-empty set structure. This gives G(x) = e x, H(x) = e x 1 ad F (x) = G(H(x)) = e ex 1. (2) The previous example admits a multitude of variatios. For istace, we ca cout partitios of a -elemet set ito blocks with more tha oe elemet by replacig H(x) with the geeratig fuctio for the trivial structure of set of more tha oe elemet, which is e x x 1. This gives the geeratig fuctio F (x) = e ex 1 x I a similar vei, we see that the geeratig fuctio for partitios with oly odd-size blocks is F (x) = e sih(x),

7 while the geeratig fuctio for partitios with oly eve-size blocks is F (x) = e cosh(x) 1. (3) Suppose you are asked to rate your prefereces amog differet foods, or sports, or whatever. You may prefer some to others, but there might also be oes you like equally well. Thus your rakig may ot be a total orderig of all the items, but rather a partitio of the items ito idifferece classes, with a liear orderig o the set of classes. Such a structure o the set of items is called a preferece orderig. We ca regard a preferece orderig as a composite structure cosistig of a partitio, a liear orderig o the set of blocks, ad a trivial o-empty set structure o each block. We have already foud the geeratig fuctio G(x) = 1/(1 x) for liear orderigs. Applyig the compositio priciple with H(x) = e x 1, we get the geeratig fuctio for preferece orderigs F (x) = 1 1 (e x 1) = 1 2 e x. Sice this is a ew cocept we have ot ecoutered before, it is iterestig to evaluate the first few terms of the geeratig fuctio. This ca be doe by substitutig for e x the first few terms of its Taylor series ad usig log divisio to get 1 2 e x = 1 1 x x 2 /2 x 3 /6 = 1 + x + 3x2 /2 + 13x 3 /6 +. Thus there is 1 preferece order o the empty set, 1 preferece order o a set of oe item, 3 possible preferece orders o a set of 2 items, ad 13 o a set of 3 items. For two items, the three possibilities are ({a, b}), ({a}, {b}), ad ({b}, {a}). For three items, you may wish to pause ad list the 13 possibilities yourself. (4) Here is a a approach to coutig permutatios differet tha the oe we used earlier. Before, we idetified permutatios with liear orderigs. This time, we idetify permutatios of a set A with bijectios from A to A, ad make use of the decompositio of a permutatio as a product of disjoit cycles. We defie a cyclic orderig of a -elemet set to be a permutatio of its elemets cosistig of a sigle cycle of legth. Note that there are ( 1)! cyclic orderigs of a -elemet set. We shall agree that there is o cyclic orderig of the empty set. Let L(x) be the expoetial geeratig fuctio for cyclic orderigs. Sice we kow how may cyclic orderigs there are, we ca write this out explicitly as L(x) = ( 1)! x! = x /. =1 Next cosider ay permutatio of A = {1,..., } expressed i cycle otatio. To choose such a permutatio we ca first choose a partitio of A, the arrage each block ito a cycle. O the set of cycles there is o further structure that is, we have a trivial structure. Applyig the compositio priciple, with G(x) = e x ad H(x) = L(x), we fid that the geeratig fuctio for permutatios is F (x) = e L(x). However, we already kow that the permutatio geeratig fuctio is equal to 1/(1 x), so we get the idetity ad hece e L(x) = 1/(1 x), =1 L(x) = log 1/(1 x) = log(1 x).

8 I this case, we already kew the aswer to the coutig problem, ad we have used it to derive a idetity. Namely, we have foud the power series expasio log(1 x) = x / = x + x2 2 + x3 3 + by purely combiatorial meas, without usig calculus. Of course, this is a formal power series expasio, ad tells us othig about the domai of covergece o the right had side. 7. The derivative priciple Derivative priciple: Suppose a f-structure o a set A is equivalet to a h-structure o the set A { } formed by augmetig A with a extra elemet. The Here are some examples: F (x) = H (x). (1) Suppose we liearly order the elemets of A plus a extra elemet. The positio of i the orderig cuts A ito two subsets, so such a structure is equivalet to partitioig A ito blocks A 1 ad A 2, ad puttig a liear order o each block. Writig F (x) for the geeratig fuctio for liear orderigs, we obtai the differetial equatio F (x) = F (x) 2. Sice F (0) = 1, we ca solve this to get the idetity F (x) = 1/(1 x) i yet aother way. (2) We will cout alteratig permutatios of {1,..., } for every. By this we mea a permutatio which has the form a 1 < a 2 > a 3 < a 4 > < a 1 > a i oe-row otatio. Note that we ca oly have the above patter if is odd. I particular, we agree that there is o empty alteratig permutatio, ad that the uique permutatio of {1} is alteratig. Let F (x) be the expoetial geeratig fuctio coutig alteratig permutatios. By listig them, you ca verify that there are 2 alteratig permutatios of {1, 2, 3} (amely 132 ad 231) ad 16 of {1, 2, 3, 4, 5}, so the first few terms of F (x) are F (x) = x + 2x 3 /6 + 16x 5 / Now cosider the problem of choosig a alteratig permutatio o umbers {1,..., +1}, which we thik of as A = [] = {1,..., } augmeted with the extra elemet + 1. Of course alteratig permuatios o A { + 1} oly exist whe is eve. By the derivative priciple, the geeratig fuctio for alteratig permutatios o A { + 1} is F (x). Note that sice F (x) cotais oly odd powers of x, it follows that F (x) cotais oly eve powers of x, as it should. Whe > 0, to choose a alteratig permuatio o A { + 1}, you must put some of the umbers 1,..., before + 1 ad the remaiig umbers after + 1. Sice a alteratig permutatio begis with a icrease ad eds with a decrease, + 1 ca t be at either ed. Thus the portios before ad after + 1 must both be o-empty. I the portio before + 1 the last step must be a decrease, sice the step to + 1 is automatically a icrease. Thus the first portio is a alteratig permutatio. By similar reasoig, so is the secod portio. Thus the set 1,..., receives a product structure: it is partitioed ito two parts, each arraged ito a alteratig permutatio. The correspodece betwee such structures o 1,..., ad alteratig permutatos o 1,..., + 1 is clearly bijective. By the multiplicatio priciple, the geeratig fuctio for such structures is F (x) 2, ad addig 1 for the case = 0 we arrive at the differetial equatio F (x) = F (x)

9 Together with the iitial value F (0) = 0, this equatio determies F (x). The solutio is F (x) = ta x. Thus we have foud a combiatorial iterpretatio of the coefficiets of the power series for ta x it is the expoetial geeratig fuctio for alteratig permutatios. By similar reasoig you ca show that sec x is the expoetial geeratig fuctio for eve alteratig permutatios (which start ad ed with a icreasig step, ad iclude the empty permutatio). 8. Proof of the priciples I this sectio we will prove the coutig priciples for expoetial geeratig fuctios. The additio priciple is obvious, so we begi with the multiplicatio priciple. Multiplicatio priciple: Let f() be the umber of g h structures o a -elemet set A. To choose a g h-structure is to choose a partitio of A ito A 1 ad A 2, with g-structure o A 1 ad h-structure o A 2. If A 1 = k, the there are ( k) choices of the partitio, g(k) choices of g-structure, ad h( k) choices of h-structure. Summig over all k we have Hece f() = k=0 ( ) g(k)h( k) = k f()/! = k=0 k=0 g(k) h( k) k! ( k)!.! g(k) h( k) k! ( k)!. This last sum is precisely the coefficiet of x i G(x)H(x), showig that F (x) = G(x)H(x). Compositio priciple: We will use the additio ad multiplicatio priciples. As part of a g h structure o A we have a partitio with some umber of blocks, say k. We will fid the geeratig fuctio for g h structures with exactly k blocks, the sum over all k. Fixig the umber of blocks to be k, let us cosider the structure o A cosistig of a ordered partitio A = A 1 A k, together with a h-structure o each block A i ad a g-structure o the set of blocks. There are always g(k) ways to choose the g structure, ad the remaiig part of the structure is just the product of k h-structures. Hece the geeratig fuctio for such ordered composite structures with exactly k blocks is g(k)h(x) k. Note that we have used here the hypothesis that H(0) = 0, that is, that there are o h-structures o the empty set. Without this hypothesis, some of the parts A i i i the product of h-structures might be empty, so that we would ot ecessarily be coutig structures with exactly k blocks. Now, there are k! ordered structures as above for each composite g h structure with k blocks, so the geeratig fuctio for the latter is g(k) H(x) k. k! Summig over all k we obtai the geeratig fuctio for composite structures F (x) = k g(k) H(x)k k! = G(H(x)).

10 Derivative priciple: The derivative of x /! is x 1 /( 1)!. Therefore if F (x) = f()x /! the the coefficiet of x /! i F (x) is f( + 1), the umber of f-structures o a -elemet set A augmeted by a extra elemet.

CS103X: Discrete Structures Homework 4 Solutions

CS103X: 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 information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In 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 information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete 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 information

Soving Recurrence Relations

Soving 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 information

I. Chi-squared Distributions

I. 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 information

Department of Computer Science, University of Otago

Department 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 information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 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 information

Here 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.

Here 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 information

SAMPLE 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. (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 information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

Solutions to Exercises Chapter 4: Recurrence relations and generating functions

Solutions to Exercises Chapter 4: Recurrence relations and generating functions Solutios to Exercises Chapter 4: Recurrece relatios ad geeratig fuctios 1 (a) There are seatig positios arraged i a lie. Prove that the umber of ways of choosig a subset of these positios, with o two chose

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

A probabilistic proof of a binomial identity

A 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 information

Infinite Sequences and Series

Infinite 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 information

Sequences and Series

Sequences 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 information

Incremental calculation of weighted mean and variance

Incremental 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 information

Asymptotic Growth of Functions

Asymptotic 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 information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE 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 information

5.3. Generalized Permutations and Combinations

5.3. Generalized Permutations and Combinations 53 GENERALIZED PERMUTATIONS AND COMBINATIONS 73 53 Geeralized Permutatios ad Combiatios 53 Permutatios with Repeated Elemets Assume that we have a alphabet with letters ad we wat to write all possible

More information

5 Boolean Decision Trees (February 11)

5 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 information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 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 information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE 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 information

Permutations, the Parity Theorem, and Determinants

Permutations, the Parity Theorem, and Determinants 1 Permutatios, the Parity Theorem, ad Determiats Joh A. Guber Departmet of Electrical ad Computer Egieerig Uiversity of Wiscosi Madiso Cotets 1 What is a Permutatio 1 2 Cycles 2 2.1 Traspositios 4 3 Orbits

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 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 information

Convexity, Inequalities, and Norms

Convexity, 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 information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 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 information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties 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 information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>

Factoring 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 information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

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 information

Your 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:

Your 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 information

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

More information

Trigonometric 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

Trigonometric 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 information

MARTINGALES AND A BASIC APPLICATION

MARTINGALES 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 information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Basic Elements of Arithmetic Sequences and Series

Basic 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 information

Elementary Theory of Russian Roulette

Elementary 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 information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.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 information

How To Understand The Theory Of Coectedess

How To Understand The Theory Of Coectedess 35 Chapter 1: Fudametal Cocepts Sectio 1.3: Vertex Degrees ad Coutig 36 its eighbor o P. Note that P has at least three vertices. If G x v is coected, let y = v. Otherwise, a compoet cut off from P x v

More information

Chapter 7 Methods of Finding Estimators

Chapter 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 information

Determining the sample size

Determining 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 information

THE HEIGHT OF q-binary SEARCH TREES

THE HEIGHT OF q-binary SEARCH TREES THE HEIGHT OF q-binary SEARCH TREES MICHAEL DRMOTA AND HELMUT PRODINGER Abstract. q biary search trees are obtaied from words, equipped with the geometric distributio istead of permutatios. The average

More information

PERMUTATIONS AND COMBINATIONS

PERMUTATIONS AND COMBINATIONS Chapter 7 PERMUTATIONS AND COMBINATIONS Every body of discovery is mathematical i form because there is o other guidace we ca have DARWIN 7.1 Itroductio Suppose you have a suitcase with a umber lock. The

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 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 information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

Overview of some probability distributions.

Overview 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 information

Chapter 5: Inner Product Spaces

Chapter 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 information

Approximating 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

Approximating 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 information

Example 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).

Example 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 information

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring

Non-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 information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 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 information

Hypothesis testing. Null and alternative hypotheses

Hypothesis 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 information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive

More information

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series 8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series

More information

1 Correlation and Regression Analysis

1 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 information

0.7 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

0.7 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 information

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments

Project 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 information

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern. 5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers

More information

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

More information

Definition. 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

Definition. 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 information

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

More information

Output Analysis (2, Chapters 10 &11 Law)

Output 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 information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

1 Computing the Standard Deviation of Sample Means

1 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 information

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:

Chapter 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 information

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number.

GCSE 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 information

5: Introduction to Estimation

5: 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 information

THE ABRACADABRA PROBLEM

THE 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 information

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph.

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph. 4. Trees Oe of the importat classes of graphs is the trees. The importace of trees is evidet from their applicatios i various areas, especially theoretical computer sciece ad molecular evolutio. 4.1 Basics

More information

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2 74 (4 ) Chapter 4 Sequeces ad Series 4. SEQUENCES I this sectio Defiitio Fidig a Formula for the th Term The word sequece is a familiar word. We may speak of a sequece of evets or say that somethig is

More information

Chapter 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 information

Analysis Notes (only a draft, and the first one!)

Analysis Notes (only a draft, and the first one!) Aalysis Notes (oly a draft, ad the first oe!) Ali Nesi Mathematics Departmet Istabul Bilgi Uiversity Kuştepe Şişli Istabul Turkey aesi@bilgi.edu.tr Jue 22, 2004 2 Cotets 1 Prelimiaries 9 1.1 Biary Operatio...........................

More information

Part - I. Mathematics

Part - I. Mathematics Part - I Mathematics CHAPTER Set Theory. Objectives. Itroductio. Set Cocept.. Sets ad Elemets. Subset.. Proper ad Improper Subsets.. Equality of Sets.. Trasitivity of Set Iclusio.4 Uiversal Set.5 Complemet

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

How To Solve The Homewor Problem Beautifully

How 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 information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL 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 information

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if

More information

Notes on Combinatorics. Peter J. Cameron

Notes on Combinatorics. Peter J. Cameron Notes o Combiatorics Peter J Camero ii Preface: What is Combiatorics? Combiatorics, the mathematics of patters,, helps us desig computer etwors, crac security codes, or solve sudous Ursula Marti, Vice-Pricipal

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

7.1 Finding Rational Solutions of Polynomial Equations

7.1 Finding Rational Solutions of Polynomial Equations 4 Locker LESSON 7. Fidig Ratioal Solutios of Polyomial Equatios Name Class Date 7. Fidig Ratioal Solutios of Polyomial Equatios Essetial Questio: How do you fid the ratioal roots of a polyomial equatio?

More information

Fast Fourier Transform

Fast Fourier Transform 18.310 lecture otes November 18, 2013 Fast Fourier Trasform Lecturer: Michel Goemas I these otes we defie the Discrete Fourier Trasform, ad give a method for computig it fast: the Fast Fourier Trasform.

More information

Maximum Likelihood Estimators.

Maximum 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 information

Hypergeometric Distributions

Hypergeometric 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 information

Building Blocks Problem Related to Harmonic Series

Building 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 information

Solving Logarithms and Exponential Equations

Solving Logarithms and Exponential Equations Solvig Logarithms ad Epoetial Equatios Logarithmic Equatios There are two major ideas required whe solvig Logarithmic Equatios. The first is the Defiitio of a Logarithm. You may recall from a earlier topic:

More information

The 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 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 information

Class Meeting # 16: The Fourier Transform on R n

Class Meeting # 16: The Fourier Transform on R n MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Confidence Intervals for One Mean

Confidence 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 information

4.3. The Integral and Comparison Tests

4.3. The Integral and Comparison Tests 4.3. THE INTEGRAL AND COMPARISON TESTS 9 4.3. The Itegral ad Compariso Tests 4.3.. The Itegral Test. Suppose f is a cotiuous, positive, decreasig fuctio o [, ), ad let a = f(). The the covergece or divergece

More information

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4 GCE Further Mathematics (660) Further Pure Uit (MFP) Tetbook Versio: 4 MFP Tetbook A-level Further Mathematics 660 Further Pure : Cotets Chapter : Comple umbers 4 Itroductio 5 The geeral comple umber 5

More information

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value Cocept 9: Preset Value Is the value of a dollar received today the same as received a year from today? A dollar today is worth more tha a dollar tomorrow because of iflatio, opportuity cost, ad risk Brigig

More information

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k.

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k. 18.409 A Algorithmist s Toolkit September 17, 009 Lecture 3 Lecturer: Joatha Keler Scribe: Adre Wibisoo 1 Outlie Today s lecture covers three mai parts: Courat-Fischer formula ad Rayleigh quotiets The

More information

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution

University 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 information

CHAPTER 4: NET PRESENT VALUE

CHAPTER 4: NET PRESENT VALUE EMBA 807 Corporate Fiace Dr. Rodey Boehe CHAPTER 4: NET PRESENT VALUE (Assiged probles are, 2, 7, 8,, 6, 23, 25, 28, 29, 3, 33, 36, 4, 42, 46, 50, ad 52) The title of this chapter ay be Net Preset Value,

More information

HOW MANY TIMES SHOULD YOU SHUFFLE A DECK OF CARDS? 1

HOW MANY TIMES SHOULD YOU SHUFFLE A DECK OF CARDS? 1 1 HOW MANY TIMES SHOULD YOU SHUFFLE A DECK OF CARDS? 1 Brad Ma Departmet of Mathematics Harvard Uiversity ABSTRACT I this paper a mathematical model of card shufflig is costructed, ad used to determie

More information

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork Solutios to Selected Problems I: Patter Classificatio by Duda, Hart, Stork Joh L. Weatherwax February 4, 008 Problem Solutios Chapter Bayesia Decisio Theory Problem radomized rules Part a: Let Rx be the

More information