Proofs of identities in FM

Size: px
Start display at page:

Download "Proofs of identities in FM"

Transcription

1 Proofs of dettes FM O The Floor 9 May 6 Bomal expaso (P) ( + x) k + (k )! x for k Q Proof: Notce, at frst, that for oegatve tegers k ad, (k ) For ths s also equvalet to! ( ) k k!!(k )! k(k ) (k + )! We temporarly exted ths otato ( ) k of to all real umbers k ad all oegatve tegers, so what we eed to prove s ( ) k ( + x) k x We splt the problem to few steps, each expadg the set of the umbers for whch the above statemet s true k N Assume k sce k s trval Wrte the expresso as the product of k detcal factors + x, ad we kow that for [, k] we have ( k ) ways to choose x for tmes ad for k tmes Ths yelds the coeffcet of x as that the term (k j) j! ( k ) For x where > k smply ote cotas factor whe j k, so we are doe for ths case k Z Proof: Let s do ducto, each tme reducg k For base case k otce that +x s equal to the sum to fty x+x x + ( ) x ( )( ) ( ) x ()() () ( ) x

2 ( ) k Iductve step: Suppose that ( + x) k x for some k < Dfferetatg both sdes yelds k( + x) k x, or ( + x) k k ( ) k ( ) x k k(k ) ( k + ) x (k ) ( k + ) ( ) k x x k ( ) ( ) k x, as desred k Q ( ( ) ) ( k ( ) ( l ( ) k + l Proof: Frst, we prove that x )x )x Notce ( that ths detty already holds for k, l Z, sce from the frst two subproblems we have ( ) ) ( k ( ) ( l ( ) k + l x )x ( + x) k ( + x) l, whle )x ( + x) k+l To prove ths, we eed to verfy that for each, coeffcet of x s the same for LHS ( ) k + l ( )( ) k l ad RHS For RHS t s For LHS ths s Observe also that ( ) k + l ( )( ) k l s a polyomal varables k ad l f s fxed, so ame t P (k, l) Our am s to prove that P (k, l) s detcally zero The fact P (k, l) for k, l Z already follows from above Now fx k as arbtrary teger ( ) k + l ( )( ) k l ad vary l, we kow that s a polyomal varable l ad has degree at most However, every teger s the root of ths moovarable polyomal, t has ftely may roots ad hace must be detcally zero Also that for a k, l R, P (k, l) P (l, k) (P s a symmetrc polyomal) so we ca assert that P (k, l) wheever oe of k, l s a teger Now tur back to P (k, l) aga, ad fx k as ay real umber ad vary l (so P (k, l) s aga a polyomal wth varable l), ad ths tme P (k, l) for ay teger l (aga!) Therefore ths moovarable polyomal must be detcally zero, aga, ad we have P (k, l) for ay par of k, l R ( Fally, let k p q, ad ( ) ) ( k ( ) ( l ( ) k + l x )x )x meas that ( ( ) ) q ( k ( ) ( kq ( ) p x )x )x ( + x) p (sce p s a teger ad ( ( ) k we already prove ths grad detty true for all tegers) Ths meas )x ( + x) p q ( + x) k, or possbly ( + x) k f q s eve Nevertheless, comparg the costat term (LHSRHS) yelds that ( + x) k s mpossble, so we are doe Commet : It s possbe to establsh P (k, l) by algebrac expaso, but too messy Commet : We ca jump from problem drectly to problem, but the proof of problem shows the beauty of dfferetato

3 Calculus If a polyomal a x + a x + + a has roots z, z,, z k, each wth multplcty b, b,, b k respectvely (so b + b + + b k ad b ), the the equato d y a dx + a d y dx + + a dy dx + a y has geeral soluto y A j x j e zx for,,, k ad j,, b ad for A j arbtrary costat Proof: Name the polyomal P Frst, otce that ths geeral soluto has exactly parameters (or degree of freedom) because we ca decde arbtrarly o what should the values f(), f (), f (), f ( ) () ad let the rest follow accordg to our dfferetal equato (we ca t go further tha ths sce the dfferetal equato also mples d +k y a dx +k + a d +k y dx +k + + a d k+ y dx k+ + a d k y for ay k ) ad these degrees dxk of freedom gves parameters, accordg to Maclaur s theorem Hece t suffces to show that x j e zx works (we ca omt ay costat sce that wll oly multply LHS by A j Now let s vestgate what happes as we dfferetate x k e ax geeral We clam, by ( ) ducto, that d dx (xk e ax )a x k e ax + a k(k ) (k + )x k e ax (otce that whe > k such terms cota factor k (k + ) + so t ca be dsregarded ( other words, o term has factor x j e ax for j < ) d ( ) Base case s trval Now assume that dx (xk e ax )a x k e ax + a k(k ) (k + )x k e ax, the d+ dx + (xk e ax ) d ( ) dx (a x k e ax + a k(k ) (k ( ) + )x k e ax )a(a x k e ax ) + ka x k e ax + a(a k(k ) (k + )x k e ax ) + ( ) (k )(a k(k ) (k + )x k e ax ) Now the term x k e ax has coeffcet a + ad x k e ax has coeffcet a + k(k ) (k + )+ a + k(k ( ) ( ) ( + ) (k +) )a + k(k ) (k +) so d+ dx + (xk e ax )a + x k e ax + + ( ) + a + k(k ) (k + )x k e ax, cosstet wth our hypothess To complete our proof, we eed to verfy that the coeffcet of x k e ax s s the dfferetal ( ) equato (for k) Now the coeffcet of x k e ax d dx (xk e ax ) s a k(k ) (k + )x ( k ) e ax, so for a z j (a root of the orgal ( polyomal) ) wth k b j we eed a zj k(k ) (k + ) + a zj k(k ) (k + ) + ( ) ( ) + a zj k(k ) (k + ) + a zj k(k ) (k + ) (Of course ( ) k for k < l) Observe that we actually assumed that z j ; ths lmt case l ca be establshed easly wherby the geeral soluto cotas term x ɛ for some ɛ, (so

4 the polyomal s dvsble by x ɛ+ ad a for ɛ But d a dx xɛ for ay ( ) a p z p p j p d dx xɛ for > ɛ so Now wth ths assumpto the above s same as provg that To prove ths, observe that the root x j has multplcty of more tha k tmes, ad k Ths meas that (x z j ) k+ dvdes the polyomal P, ad t s well-kow that d α dx α P (x) s dvsble by (x z j) k+ α for α k so substtutg to alpha yelds that d α dx α P (x) s dvsble by (x z j) k+ (ad k + ) So d P (x) whe dx x z j Also observe that the t-th dervatve of x u s u(u ) (u t + )x u t, so d d P (x) dx dx a p x p p(p ) (p + )a p x p!p(p ) (p + p p p ( ) p )a p x p! a p x p (for x z j ), as clamed Matrces p Row space ad colum space of ay matrx have the same dmeso Proof: Let dmeso of colum space be WLOG let a a m a a a m, a a a m,, be parwse learly depedet The for j >, a j λ kja k for some real umbers λ kj a k Multplyg each elemet row by a a a a a m a m a { a a { a m a m { λ kj a k } λ kj a k a } λ kj a mk a m } we have: where {} meas that j rus from +, +,, g where g s the umber of colums of ths matrx For covece we ame j as aythg betwee + ad g, clusve (We assume that a If t s, the just swap the whole row wth some lower rows wth ozero frst etry) 4

5 Now ref for the frst elemet each row yelds a a a a a m a m a a { a a a { a m a m a { λ kj a k } λ kj ( a k a a k )} λ kj ( a mk a m a k )} If a xy axy a x a y the the orgal matrx becomes a a a k { λ kj } a a { λ kj a k } a m a m { λ kj a mk } Replacg each a xy wth a xy yelds we ca assume that the frst elemet of eaach (except the frst) row s zero Repeatg the ref process yelds: a a ( ) a a k { λ kj } a a ( ) a { λ kj a k } a { λ kj a k } But otce that the frst elemets of j-th row (j > ) are all zero It follows that λ kj a k ( ) must be zero So all other elemets must be zero ad we have colums osstg etrely of zeroes after the -th row Ths yelds that, the dmeso of row space caot exceed (some of the rows to mght be etrely zero, whch case we kow that dmeso of row space < ), e caot exceed the dmeso of colum space Smlarly the dmeso of colum space caot exceed the dmeso of row space Hece they are equal Multplcatve assocatvty of matrces: (AB)C A(BC) Proof: Let s check the dmeso of each matrx for the equato to be vald Let the 5

6 dmesos of A, B, C be a a, b b ad c c, respectvely For AB to be vald we must have a b ad the resultg matrx has dmeso a b For (AB)C to be vald we eed b c ad the matrx at LHS has dmeso a c A smlar check at RHS yelds a b, b c ad resultg matrx wth dmeso a c, hece the dmeso check s doe Now let s vestgate every sgle etry x at -th row ad j-th colum Deote [ab] xy as the elemet of -th row ad j-th colum of matrx AB; defe [bc] xy smlarly Now b c b c a b for (AB)C ths etry s [ab] k c kj ( a d b dk )c kj a d b dk c kj d d a, k b a b a b b c For A(BC) ths etry s a d [bc] dj a d ( b dk c kj ) a d b dk c kj d d d a, k b Hece the every correspodg etres of (AB)C ad A(BC) are equal QED Gve a matrx P wth o-zero determat, ad r, r, r are vectors such that P r T r T (where T stads for traspose) The P ( ) r r r r r r r T det(p ) Proof: Let P ( ) s s s Observe that r s j for j ad otherwse Ths meas that s s a scalar multple of r + r + (dces take modulo ) (Why? s s perpedcular to both r + ad r + ) Now let costats k be that s k (r + r + ), so k r (r + r + ) However, t s well-kow that r (r r ) r (r r ) r (r r ) det(p ) so k k k det(p ) ad we are doe To verfy ths for k, f r a a, r a a a, r a a a a a a a a a + a a a a a a det the r (r r ) a a a a + a a a a a a a a det(p ) 4 If a matrx Q has learly depedet egevectors r, r, r ad correspodg egevalues a, a,, a, the Q P DP, where P (r r r ) ad D s the dagoal matrx wth a at -th row ad -th colum Proof: Deote, aga, T as the traspose of vector If P s T s T s T the r s j f j ad otherwse Now let matrx x be (r ), the P x has for all elemets except the -th etry, whch s Ths meas DP x D has for all elemets 6

7 except the -th etry, whch s a Therefore (r r r ) a a (r ) a x Ths meas that P DP has exactly the same egevectors (wth correspodg egevalues) as Q, ad we show that such matrx s uque To ths ed, let s show that P DP Q Now we have Q(r ) P DP (r ) a (r ), so (P DP Q)(r ) for,,, But sce r, r,, r are learly depedet, we kow that the dmeso of ull space of P DP Q s, or the rak of ths matrx s Hece P DP Q s a zero matrx QED 5 Let A be a m matrx If there exsts a matrx B such that AB ad BA are detty matrces, the m Proof: If AB ad BA are both defed, the the dmeso of B s m, ad AB ad BA have dmesos m m ad, respectvely If m, wthout the loss of geeralty we ca assume that m < We prove that the matrx BA has rak of at most m, whch forces ts determat to be zero (ad therefore caot be detty matrx) Now let C BA, ad deote ts etry vector maer (v v v ) The v b a + b a + + b m a m b a + b a + + b m a m (a w + a w + + a m w m ) where w j represets the vector b a + b a + + b m a m b j b j, or the j-th colum of matrx B Notce that every vector v b j ca be represeted as x w + x w + + x m w m, where x, x,, x R Ths meas the set {v, v,, v } ca oly spa at most m dmesos, e the dmeso of colum space of ths matrx BA s at most m 6 det(a) det(b) det(ab), where A, B are matrces Proof: Let C AB, ad deote a j as the etry of -th row ad j-th colum of A Defe b j ad c j smlarly The term a pq b rs wll appear as a term C ff q r, whch case t wll be cotaed the etry c ps Moreover, to be cosdered to the determat of C, each term must be the form of c σ() a j b jσ(), where {σ() } s the permutato of {,,, } Idvdually speakg, the product of sums above comprses terms the form a α() b α()σ(), where α(), α(), α() s a sequece of umbers satsfyg α(j), j [, ] Notce also that det(c) 7 j c c c

8 ( ( ) N(γ) c γ() ), where he sum s take across all permutatos γ ad N(γ) s the umber of versos γ Ths meas that the coeffcet of a α() b α()σ() det(c) s well-defed, e the sums of coeffcet of ths term all the terms ( ) N(γ) c γ() We show that f {α() } s ot a permutato of {,,, } (e some of them are equal), the the product a α() b α()σ() has total coeffcet zero det(c) Ad f t s a permutato (meag each term s dfferet), the the coeffcet s + f {σ() } s a eve permutato, ad - otherwse Now for each partcular permutato σ, the terms the (mult-)set {a j b jσ(), j } s parwse dstct, whch meas the term a α() b α()σ() has coeffcet of exactly the expaso of c σ() a j b jσ() (Ie every term the form j )x a xy b yσ(x) (σ permutato of x) ca oly appear at most oce ths expaso) Defe the occureces of the term a α() b α()σ() as the total umber of the permutato σ such that ths term s a term the expaso of c σ () The for each k we ame f(k) as the umber of [, ] st α() k We show that the occureces of a α() b α()σ() s f()! Ideed, ths s equvalet to sayg that a α ()b α ()σ () a α() b α()σ(), or smply b α()σ () b α()σ() as we ca take α α for the purpose of establshg ths proof of our clam Now defe S k as {, α() k} (for k,,, ) ad what we eed s {σ () S(k)} {σ() S(k)}, k [, ] Now σ σ s deftely a soluto, ad for each such k we kow that {σ() S(k)} has f(k)! solutos For each k, there are f(k)! possbltes to very σ, ad multplyg ths for all such k yelds the aswer f()!f()! f()! To fsh ths lemma, we eed to prove that f f(k) for some k, the amog all f()! permutatos σ, exactly half of them s odd ad half eve Ths meas that there must be f()! occureces of a α() b α()σ() that cotbutes postvely to the determat of C (e have coeffcet + ths determat calculato) ad f()! that cotrbutes egatvely, whch gves a overall coeffcet of zero We eed the followg well-kow lemma (ot hard to prove, though) : If teger x, swappg ay two elemets a permutato of x dstct objects wll chage the permutato from odd to eve, ad vce versa Ths allows us to par up all feasble permutatos σ to f()! pars such that ech par has a odd permutato ad eve permutato Let j be a dex [, ] such that f(j), ad choose j ad j such that α(j ) α(j ) j The for each permutato σ, par t up wth aother permutato σ such that: 8

9 σ ( ) σ ( ) σ ( ) σ ( ) σ () σ (), [, ]\{, } Clearly, f σ fulflls the clam that {σ () S(k)} {σ() S(k)}, k [, ] the σ also fulflls the clam that {σ () S(k)} {σ() S(k)}, k [, ] Moreover, each par s dsjot (meag o elemet ca exst two groups) sce the other elemet (e permutato) the same par wth a permutato s defed uquely before Ths completes our bjecto (ad hece our clam) If f(k) for all k, the there s oly oe such occurece of a α() b α()σ(), or σ σ s the oly possble permutato The two prevous terms have coeffcet det(c) ff c σ() has a coeffcet det(c), e σ s a eve permutato or N(σ) s eve Fally, let s prove that for f α s a permutato, the the coeffcet of det(c) s the product of the coeffcet of a α() b α()σ() a α() det(a) ad the coeffcet of b α()σ() det(b) Let I be the detty permutato, e I(x) x, x [, ] De- ote also the permutato that brgs α to σ as ω (meas f α() j ad σ() k, the ω(j) k Try to thk ω as σα ) Now we kow that f a permutato σ s eve, the I ca be mapped to σ by swappg two eghbourg elemets oly by a eve umber of tmes (ad vce versa) I other words, f N deotes the mmum umber of swappgs eeded, the we ca map I to σ by N(α) + N(ω) of eghbourg swappgs (frst N(α) swappg from I to α, the N(ω) swappgs from α to σ) Ths etals N(σ) N(α) + N(ω) (mod ), or ( ) N(σ) ( ) N(α) ( ) N(ω) Sce b α()σ() b ω(), we have: coeffcet of a α() b α()σ() s ( ) N(σ), coeffcet of a α() s ( ) N(α), ad b α()σ() s ( ) N(ω) Ths proves our clam, ad det(c) ( ) N(σ) c σ() ( ) N(σ) a α() b α()σ() (σ ay permutato, α ay - tuple) ( ) N(σ) a α() b α()σ() (same defto for σ but restrct α to permutatos, sce the other case has coeffcet zero ad ca be dsregarded) ( ) N(α) a α() ( ) N(ω) b ω() ( ) N(α) a α() ( ) N(ω) b α()σ() det(a) det(b), as desred 4 Cetrod of bodes Sold hemsphere wth radus r Aswer: 8r from the cetre, ad lyg o the le through cetre ad perpedcular to the plae of the great crcle Proof: Let the radus of the hemsphere be r, the the equato of ths hemsphere s just y r x from to r havg rotated 6 We kow that ȳ To fd x smply 9

10 take the fracto π xy dx π y dx x(r x ) dx r x dx [xr x4 4 ]r [xr x ]r r 4 4 r 8 r Hemsphercal shell wth radus r Aswer: r from the cetre, ad lyg o the le through cetre ad perpedcular to the plae of the great crcle π ( ) r dy xy + dx dx Proof: same equato as above for x ad y, but ow we eed x π ( ) r dy y + dx dx Observe that d ( ) dx ((r x ) 5 x dy ) (r x so y + (r x ) 5 r ) 5 dx (r x ) r The orgal tegral the becomes x xr dx [r( )]r r dx [r(x)] r r r r Crcular sector wth radus r ad subteded agle α r s α Aswer: from the cetre of the crcle (e sector) ad lyg o the agle bsector α of the sector Proof: Same equato as above, for x from r cos α to r, ad y ta α for x from r cos α xy dx to r cos α Now x y dx x ta α dx + r cos α x r x dx cos α x ta α dx + r cos α r x dx Now to deal wth the secod tegral umerator we eed the substtuto x r cos θ, ad r we have dx r s θdθ r cos α x r x dx α r cos θ r r cos θr s θdθ [ ] α α r cos θ s θdθ r s θ r s α (We kow from the area of sector that the deomator s r α) cos α x ta α dx r s α cos α Ths yelds x r s α [ x ta α Addg the two terms up yeld r s α ] r cos α (r cos α) s α cos α (cos α + s α) r s α r s α r α α It s obvous that the cetre of mass of ths sector les o the agle bsector of the sector due to symmetry, hece have gradet cos α rom cetre O (the org) The legth from the org to the cetre of mass has therefore legth r s α r s α α cos α α 4 Crcular arc wth radus r ad subteded agle α Aswer: r s α from the cetre of the crcle (e sector) ad lyg o the agle bsector α of the sector Proof: The equato s gve by y r x, x from rcos α to r As show above, ( ) r + dy xr r dx Here, x r cos α r x dx r x r r The deomator s smply the arc r cos α r dx x legth rα, whle the umerator s equal to [ r r x ] r r cos α r r r cos α r s α Therefore x r s α Aga, dvde t by cos α to fd he dstace from the rα cetre ad we have r s α rα cos α r s α α

11 5 Tragular lama ABC Aswer: Let the mdpot of BC to be M, the the cetre of mass s the pot G o segmet AM, satsfyg AG : GM : Proof: We prove that ths cetrod must le o all medas of a tragle by establshg that the magtude of momet wth respect to AM at both sdes of AM must be the same To do ths, we cut the tragle to very ty strps, each oe parallel to AM, ad prove that for each strp, we ca fd aother strp o the other sde of the meda that has the same legth (or same mass) ad wth the same dstace from the le AM We eed to prove frst that the umber of strps o both sdes must the same, gve the each has wdth d (fsmal) Now, the product of d ad the total umber of strps o sde BM s the perpedcular dstace from B to AM, whch s BM s AMB; the product of d ad the total umber of strps o sde CM s the perpedcular dstace from C to AM, whch s CM s AMC However, BM CM (M s the mdpot of BC) ad s AMB s AMC (these agles are supplemetary) so ths clam s prove Next, cosder a pot P o sde BM, ad Q a pot o sde AM such that P Q s parallel to AM Next, reflect P across M to get P, ad let Q be a pot o AC such that P Q AM Now P Q AM BP BM CP CM P Q AM so P QP Q (The frst equvalece s because tragles BQP ad BAM are smlar; the secod equvalece follows whe BP CP by the defto of reflecto ad BM CM; the thrd equvalece s the cosequece of the fact that tragles CAM ad CQ P are smlar) Fally, the dstace betwee strp P Q ad le AM s P M s BAM ad dstace betwee strp P Q ad le AM s P M s CAM It s easy to verfy that the are the same The smlar proof above ca be used to verfy that the other medas cota the cetre of mass of tragle ABC as well 5 Momet of erta Th rod of mass m legth r about the perpedcular axs through the mdpot Aswer: mr r Proof: Momet of ertam x x dx [ m ]r r r dx [x] r m r r mr r Rectagular lama of mass m ad dmesos a b about the perpedcular axs through the cetre of mass Aswer: m(a + b ) Proof: Cosder al the partcles o the rectagular lama (wth mass dm), so the mass of the partcles s actually dm ad the momet of erta s (x + a x a, b y b a x a, b y b y )dm Turg ftely may partcles to double tegrato (because there are two dmesos!) we have a a ( b b dy) dx for mass, ad a a ( b b x + y dy) dx for momet of erta Now ( b b dy)[y]b b b so a a ( b b dy) dx a a b dx [bx]a a 4ab ( b b x + y dy)[x y + y ]b b x b + b so a a x b + b dx b + xb [x ] a a 4a b + 4ab ( a + b ) 4ab Dvdg t by 4ab ad multplyg by m yelds the momet

12 ( a + b ) of erta as m Dsc/sold cylder wth mass m ad radus r about the axs pepedcular to the crcular plae ad pasg through ts cetre mr Aswer: Proof: Let the heght of sold cylder be h ( the evet of a crcular lama we ca treat ths h as fsmal (whch does t affect our aswer ayway) Now cosder the sub-rg (or sub-cylderc shell) wth cetre the axs ad radus x, ad the surface area s deftely πxh The desred rato ow becomes whch leads to our aswer πx h dx πxh dx x dx [x4/4]r x dx [x /] r r, 4 Sold sphere of mass m ad radus r about a axs passg through ts cetre mr Aswer: 5 Proof: Treat ths sphere as x + y + z r about the z-axs, ad cosder the locus of pots of dstace d from ths z-axs Now d x + y, ad d + z r Therefore z r d or r d z r d The locus s therefore the curved face of cyldrcal shell wth radus d ad heght r d, ad the surace area s πd( r d ) 4πd r d The desred fracto s ow (x )4πx r x r dx 4πx r x dx x r x dx x Now for both de- r x dx omator ad umerator, we use the substtuto x r s θ to get dx r cos dθ, so that x r x dx [ π (r s θ)(r cos θ)(r cos θ dθ) π cos r s θ cos θ dθr ] π θ r Smlarly, x r x dx π (r s θ) (r s θ)(r cos θ)(r cos θ dθ) π r5 s θ cos θ dθ π r5 s θ( cos θ) cos θ dθ π r5 s θ cos θ dθ - π [ cos - r 5 5 θ 5 r 5 5 r 5 ] π [ cos r5 s θ cos 4 θ dθ r 5 θ r5 5 r5 r5 Summarzg above yelds the desred fracto as 5 r, whch yelds the aswer 5 Sphercal shell of mass m ad radus r about a axs passg through ts cetre mr Aswer: Proof: Treat ths shell as the equato y r x, rotated through 6 the x-axs (x rgg from r to r) Assume, too, that the x-axs s our axs of referece Now the dstace of each pot from ths axs s ts y- coordate, so for each y, x ± r x ( ) dy For each of these two x, a crcle wth crcumferece πy + s geerated upo dx reoluto (recall how we foud surface area of revoluto of a graph!) As always, + ( ) dy ( ) r dy dx r Ths meas πy + π r x dx x r πr Ths meas r x ] π

13 ( ) dy r (y )πy + dx dx that our desred fracto s ow ( ) dy r πy + dx dx [πr x rx ]r r [πrx] r r 4πr4 4 r4 4πr r mr so the aswer s r πr(r x ) dx r πr dx 6 Perpedcular axs theorem (apples oly to a lama) Proof: We assume that the lama s o the x y plae, ad Let f(x, y) be the desty at pot x, y Now the mass of ths lama s (gorg lmts) ( f(x, y) dx) dy For each partcle at pot (x, y), the dstace from x-axs s y ad the dstace from y-axs s x Its dstace from z-axs s x + y (e dstace from the org) Therefore I z ( (x +y )f(x, y) dx) dy ( x f(x, y) dx+ y f(x, y) dx) dy ( x f(x, y) dx)dy + ( y f(x, y) dx) dyi y + I x, as desred 7 Parallel axs theorem (apples to a sold ad a lama) Proof: Let the desty at pot (x, y, z) be f(x, y, z), ad ts mass s m ( ( f(x, y, z) dx) dy) dz (aga, droppg lmts) Assume, WLOG, that the cetre of mass les at pot (,,), meag that ( ( xf(x, y, z) dx) dy) dz ( ( yf(x, y, z) dx) dy) dz ( ( zf(x, y, z) dx) dy) dz Now let the axs through the cetre of mass to be the z-axs, ad the other axs to be x a, y b The dstace of sold from the frst axs s x + y ad dstace from the secod axs s (x a) + (y b) Now the momet of erta about the secod axs s ( ( ((x a) + (y b) )f(x, y, z) dx) dy) dz ( ( (x + y + a + b ax by)f(x, y, z) dx) dy) dz ( ( (x + y )f(x, y, z) dx) dy) dz+ ( ( (a + b )f(x, y, z) dx) dy) dz - ( ( axf(x, y, z) dx) dy) dz - ( ( byf(x, y, z) dx) dy) dz I z axs + (a + b ) ( ( f(x, y, z) dx) dy) dz - a ( ( xf(x, y, z) dx) dy) dz - b ( ( yf(x, y, z) dx) dy) dz I z axs + (a + b )m -, e the momet of erta about the frst axs + mr where r s the dstace of the secod axs from the org 6 Regresso les Gve pots (x, y ),,,, The the equato of le of regresso of y o x (x x)(y ȳ) s gve by y ȳ (x x) (x x) Proof: Frst, we prove that for a set of les wth commo gradet, the sum of square of vertcal devato of each pot from the le s mmum whe the le passes through the cetre of mass of the pots Suppose that the gradet s k, ad let θ ta k be the agle (atclockwse) of the le wth the x-axs Now perform a rotato of everythg (the pots ad the le) of agle θ clockwse about ay pot, say, the org The the le ow becomes horzotal, the tal vertcal devato of each pot to the le becomes the θ -degree to the vertcal devato to the le (whch s, cos θ tmes the ew vertcal devato (or perpedcular dstace) to the le: multplato by a costat across all pots) Moreover, the cetre of mass of the pots also follows the same ( rotato (It makes ) ( prefect ) sese tutvely, but to cos α s α x rgorze the argumet, otce that rotates the pot (x, y) as α cos α y

14 gle α atclockwse If α θ the the rotato of pots (x, y ),,,, have cetre of mass ( ) ( ) cos α s α x ( ) ( ) cos α s α x, whch s α cos α s α cos α s the orgal cetre of mass havg rotated agle α) y Now let (x, y ) be our ew pots, ad sce the le s ow horzotal we ca assume that ts equato s y w, w costat Now the vertcal dstace of each pot to the le s y w so the sum of square of dstace s (y w) w w y + (y ) w y ( y ) + (y ) Now the θ degree to the ormal dstace ca be obtaed by dvdg each term by cos θ, whch s a costat across each term! Therefore, the mmum dstace ca be acheved whe w the cetre of mass y y, e passg through To fd the gradet of ths le, otce that the equato of the le must be the form y ȳ b(x x) The vertcal devato from each pot to the le s thus y b(x x) ȳ Therefore the sum of square of vertcal devato s [(y ȳ) b(x x)] b (x x) b (x x)(y ȳ) + (y ȳ) Aga, by completg the square we kow that the least square sum s acheved whe b umerator s equvalet to to (x ) ( ) x x y (x x)(y ȳ) x y Notce, also, that the (x x) ad that the deomator s equvalet 4

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

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

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

MA 524 Homework 6 Solutions

MA 524 Homework 6 Solutions MA 524 Homework 6 Solutos. Sce S(, s the umber of ways to partto [] to k oempty blocks, ad c(, s the umber of ways to partto to k oempty blocks ad also the arrage each block to a cycle, we must have S(,

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

4 Inner Product Spaces

4 Inner Product Spaces 11.MH1 LINEAR ALGEBRA Summary Notes 4 Ier Product Spaces Ier product s the abstracto to geeral vector spaces of the famlar dea of the scalar product of two vectors or 3. I what follows, keep these key

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

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

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

Centroids & Moments of Inertia of Beam Sections

Centroids & Moments of Inertia of Beam Sections RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol

More information

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

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each 01 Log1 Cotest Roud Theta Complex Numbers 1 Wrte a b Wrte a b form: 1 5 form: 1 5 4 pots each Wrte a b form: 65 4 4 Evaluate: 65 5 Determe f the followg statemet s always, sometmes, or ever true (you may

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

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

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET

AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET Abstract. The Permaet versus Determat problem s the followg: Gve a matrx X of determates over a feld of characterstc dfferet from

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

18.413: Error Correcting Codes Lab March 2, Lecture 8

18.413: Error Correcting Codes Lab March 2, Lecture 8 18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse

More information

Introduction to Matrices and Matrix Approach to Simple Linear Regression

Introduction to Matrices and Matrix Approach to Simple Linear Regression Itroducto to Matrces ad Matrx Approach to Smple Lear Regresso Matrces Defto: A matrx s a rectagular array of umbers or symbolc elemets I may applcatos, the rows of a matrx wll represet dvduals cases (people,

More information

Algorithms Theory, Solution for Assignment 2

Algorithms Theory, Solution for Assignment 2 Juor-Prof. Dr. Robert Elsässer, Marco Muñz, Phllp Hedegger WS 2009/200 Algorthms Theory, Soluto for Assgmet 2 http://lak.formatk.u-freburg.de/lak_teachg/ws09_0/algo090.php Exercse 2. - Fast Fourer Trasform

More information

α1 α2 Simplex and Rectangle Elements Multi-index Notation of polynomials of degree Definition: The set P k will be the set of all functions:

α1 α2 Simplex and Rectangle Elements Multi-index Notation of polynomials of degree Definition: The set P k will be the set of all functions: Smplex ad Rectagle Elemets Mult-dex Notato = (,..., ), o-egatve tegers = = β = ( β,..., β ) the + β = ( + β,..., + β ) + x = x x x x = x x β β + D = D = D D x x x β β Defto: The set P of polyomals of degree

More information

Exercises for Square-Congruence Modulo n ver 11

Exercises for Square-Congruence Modulo n ver 11 Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

Mathematics HL and Further mathematics HL Formula booklet

Mathematics HL and Further mathematics HL Formula booklet Dploma Programme Mathematcs HL ad Further mathematcs HL Formula booklet For use durg the course ad the eamatos Frst eamatos 04 Mathematcal Iteratoal Baccalaureate studes SL: Formula Orgazato booklet 0

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

5 Short Proofs of Simplified Stirling s Approximation

5 Short Proofs of Simplified Stirling s Approximation 5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

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

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

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

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever. 9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,

More information

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS GENERLIZTIONS OF CEV S THEOREM ND PPLICTIONS Floret Smaradache Uversty of New Mexco 200 College Road Gallup, NM 87301, US E-mal: smarad@um.edu I these paragraphs oe presets three geeralzatos of the famous

More information

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i ENUMERATON OF FNTE GROUPS OF THE FORM R ( 2 = (RfR^'u =1. 21 THE COMPLETE ENUMERATON OF FNTE GROUPS OF THE FORM R 2 ={R R j ) k -= H. S. M. COXETER*. ths paper, we vestgate the abstract group defed by

More information

QR Factorization and Singular Value Decomposition COS 323

QR Factorization and Singular Value Decomposition COS 323 QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?

More information

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

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,

More information

MATH 371 Homework assignment 1 August 29, 2013

MATH 371 Homework assignment 1 August 29, 2013 MATH 371 Homework assgmet 1 August 29, 2013 1. Prove that f a subset S Z has a smallest elemet the t s uque ( other words, f x s a smallest elemet of S ad y s also a smallest elemet of S the x y). We kow

More information

INTERNATIONAL BACCALAUREATE ORGANIZATION GROUP 5 MATHEMATICS FORMULAE AND STATISTICAL TABLES

INTERNATIONAL BACCALAUREATE ORGANIZATION GROUP 5 MATHEMATICS FORMULAE AND STATISTICAL TABLES INTERNATIONAL BACCALAUREATE ORGANIZATION GROUP 5 MATHEMATICS ORMULAE AND STATISTICAL TABLES To be used the teachg ad eamato of: Mathematcs HL Mathematcal Methods SL Mathematcal Studes SL urther Mathematcs

More information

CS286.2 Lecture 4: Dinur s Proof of the PCP Theorem

CS286.2 Lecture 4: Dinur s Proof of the PCP Theorem CS86. Lecture 4: Dur s Proof of the PCP Theorem Scrbe: Thom Bohdaowcz Prevously, we have prove a weak verso of the PCP theorem: NP PCP 1,1/ (r = poly, q = O(1)). Wth ths result we have the desred costat

More information

Lattices. Mathematical background

Lattices. Mathematical background Lattces Mathematcal backgroud Lattces : -dmesoal Eucldea space. That s, { T x } x x = (,, ) :,. T T If x= ( x,, x), y = ( y,, y), the xy, = xy (er product of xad y) x = /2 xx, (Eucldea legth or orm of

More information

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

Neville Robbins Mathematics Department, San Francisco State University, San Francisco, CA (Submitted August 2002-Final Revision December 2002) Nevlle Robbs Mathematcs Departmet, Sa Fracsco State Uversty, Sa Fracsco, CA 943 (Submtted August -Fal Revso December ) INTRODUCTION The Lucas tragle s a fte tragular array of atural umbers that s a varat

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y. .46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure

More information

= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n

= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n .. Soluto of Problem. M s obvously cotuous o ], [ ad ], [. Observe that M x,..., x ) M x,..., x ) )..) We ext show that M s odecreasg o ], [. Of course.) mles that M s odecreasg o ], [ as well. To show

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:

More information

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

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Qualifying Exam Statistical Theory Problem Solutions August 2005

Qualifying Exam Statistical Theory Problem Solutions August 2005 Qualfyg Exam Statstcal Theory Problem Solutos August 5. Let X, X,..., X be d uform U(,),

More information

Numerical Analysis Formulae Booklet

Numerical Analysis Formulae Booklet Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx

More information

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY 3 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER I STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad

More information

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

(b) By independence, the probability that the string 1011 is received correctly is Soluto to Problem 1.31. (a) Let A be the evet that a 0 s trasmtted. Usg the total probablty theorem, the desred probablty s P(A)(1 ɛ ( 0)+ 1 P(A) ) (1 ɛ 1)=p(1 ɛ 0)+(1 p)(1 ɛ 1). (b) By depedece, the probablty

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

Rademacher Complexity. Examples

Rademacher Complexity. Examples Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed

More information

A BASIS OF THE GROUP OF PRIMITIVE ALMOST PYTHAGOREAN TRIPLES

A BASIS OF THE GROUP OF PRIMITIVE ALMOST PYTHAGOREAN TRIPLES Joural of Algebra Number Theory: Advaces ad Applcatos Volume 6 Number 6 Pages 5-7 Avalable at http://scetfcadvaces.co. DOI: http://dx.do.org/.864/ataa_77 A BASIS OF THE GROUP OF PRIMITIVE ALMOST PYTHAGOREAN

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

Complex Numbers and Polynomials pages (d) If A = (6, 1), then A = (1, 6). (c) If A = (3, 5), then A = ( 5, 3).

Complex Numbers and Polynomials pages (d) If A = (6, 1), then A = (1, 6). (c) If A = (3, 5), then A = ( 5, 3). Chapter 6 Complex Numbers ad Polyomals pages 7 7... INVESTIGATION 6A 6. Gettg Started GRAPHING COMPLEX NUMBERS For You to Explore. a + + + + + + + b + + + + c + + + + + 6 + + + d + + + + + 9 + 6 8 + 6

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Generalized Linear Regression with Regularization

Generalized Linear Regression with Regularization Geeralze Lear Regresso wth Regularzato Zoya Bylsk March 3, 05 BASIC REGRESSION PROBLEM Note: I the followg otes I wll make explct what s a vector a what s a scalar usg vec t or otato, to avo cofuso betwee

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Class 13,14 June 17, 19, 2015

Class 13,14 June 17, 19, 2015 Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral

More information

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i. CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy

More information

Chapter 3. Linear Equations and Matrices

Chapter 3. Linear Equations and Matrices Vector Spaces Physcs 8/6/05 hapter Lear Equatos ad Matrces wde varety of physcal problems volve solvg systems of smultaeous lear equatos These systems of lear equatos ca be ecoomcally descrbed ad effcetly

More information

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

More information

Test Paper-II. 1. If sin θ + cos θ = m and sec θ + cosec θ = n, then (a) 2n = m (n 2 1) (b) 2m = n (m 2 1) (c) 2n = m (m 2 1) (d) none of these

Test Paper-II. 1. If sin θ + cos θ = m and sec θ + cosec θ = n, then (a) 2n = m (n 2 1) (b) 2m = n (m 2 1) (c) 2n = m (m 2 1) (d) none of these Test Paer-II. If s θ + cos θ = m ad sec θ + cosec θ =, the = m ( ) m = (m ) = m (m ). If a ABC, cos A = s B, the t s C a osceles tragle a eulateral tragle a rght agled tragle. If cos B = cos ( A+ C), the

More information

Maps on Triangular Matrix Algebras

Maps on Triangular Matrix Algebras Maps o ragular Matrx lgebras HMED RMZI SOUROUR Departmet of Mathematcs ad Statstcs Uversty of Vctora Vctora, BC V8W 3P4 CND sourour@mathuvcca bstract We surveys results about somorphsms, Jorda somorphsms,

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX

LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 6 2006, #A12 LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX Hacèe Belbachr 1 USTHB, Departmet of Mathematcs, POBox 32 El Ala, 16111,

More information

Chapter 8. Inferences about More Than Two Population Central Values

Chapter 8. Inferences about More Than Two Population Central Values Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha

More information

Chapter 4 Multiple Random Variables

Chapter 4 Multiple Random Variables Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:

More information

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

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015 Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

BIOREPS Problem Set #11 The Evolution of DNA Strands

BIOREPS Problem Set #11 The Evolution of DNA Strands BIOREPS Problem Set #11 The Evoluto of DNA Strads 1 Backgroud I the md 2000s, evolutoary bologsts studyg DNA mutato rates brds ad prmates dscovered somethg surprsg. There were a large umber of mutatos

More information

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

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d 9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

( ) ( ) A number of the form x+iy, where x & y are integers and i = 1 is called a complex number.

( ) ( ) A number of the form x+iy, where x & y are integers and i = 1 is called a complex number. A umber of the form y, where & y are tegers ad s called a comple umber. Dfferet Forms )Cartesa Form y )Polar Form ( cos s ) r or r cs )Epoetal Form r e Demover s Theorem If s ay teger the cos s cos s If

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

ρ < 1 be five real numbers. The

ρ < 1 be five real numbers. The Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace

More information

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006 Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.

More information

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

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two Overvew of the weghtg costats ad the pots where we evaluate the fucto for The Gaussa quadrature Project two By Ashraf Marzouk ChE 505 Fall 005 Departmet of Mechacal Egeerg Uversty of Teessee Koxvlle, TN

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

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

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

The conformations of linear polymers

The conformations of linear polymers The coformatos of lear polymers Marc R. Roussel Departmet of Chemstry ad Bochemstry Uversty of Lethbrdge February 19, 9 Polymer scece s a rch source of problems appled statstcs ad statstcal mechacs. I

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

arxiv:math/ v1 [math.gm] 8 Dec 2005

arxiv:math/ v1 [math.gm] 8 Dec 2005 arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko

More information

h-analogue of Fibonacci Numbers

h-analogue of Fibonacci Numbers h-aalogue of Fboacc Numbers arxv:090.0038v [math-ph 30 Sep 009 H.B. Beaoum Prce Mohammad Uversty, Al-Khobar 395, Saud Araba Abstract I ths paper, we troduce the h-aalogue of Fboacc umbers for o-commutatve

More information

13. Dedekind Domains. 13. Dedekind Domains 117

13. Dedekind Domains. 13. Dedekind Domains 117 3. Dedekd Domas 7 3. Dedekd Domas I the last chapter we have maly studed -dmesoal regular local rgs,. e. geometrcally the local propertes of smooth pots o curves. We ow wat to patch these local results

More information

Introduction to Probability

Introduction to Probability Itroducto to Probablty Nader H Bshouty Departmet of Computer Scece Techo 32000 Israel e-mal: bshouty@cstechoacl 1 Combatorcs 11 Smple Rules I Combatorcs The rule of sum says that the umber of ways to choose

More information