internship20120620195440138 - &quot C Var é/ww = A Wm(102...

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Unformatted text preview: _/" C- Var é/ww) = A Wm: (102 EUX’EMY] 21:11 am) w= ii 3-2.1“: 3) $4.211 P‘Vl! “For a.“ )c D- E[P1.(1\] 1 [(714)1'W1/7) WM! Tammi: 244M/zib 92 [/u) - 9‘: 2‘7 ‘1 1 1 1-31 “(m/1) +121 11%) (1¢"/21)+w.1/flw(141111317“) 1 PHIL)" WNW)" Q'vp) .0 /}J, ’EC‘OJ: gag-1‘05)" §Ix(I—p)fl r; . 131.1 : .L- (1- 4170)" P F '/,o Va E] 101%:th a; A 1813514qu 0" 5 I5 _ A...“ , , {/2 Y‘ET 11.. i 1‘ 3-7777 A“-.. 3.111- PMF otnIerm "3U '25qu 'r—‘H‘Ké' ‘3 Phat" '7): {/14 11163;} .l X l ‘ yfixfl 0, DHI'Wj .L . l LL 13051 U: WA, EH}: “Hit * '2" 2{V'LI) +5_(_/:__f*/"V‘1‘3)[4 71.13“ LilUuPSC'f'zl) 15111=34+wa+51m1 1:13 ‘W: 5 1‘1 70771 1 5:1 G'llH (tank), k. VflnFU vs raw“ 51 CW M thM 9F u. shut. Y. fiat» slmru'r . / W44 yam-r 100-1 ¢ Zoo-”1 may» palm-r : 100 FLDL'] 1' ‘LDa' ELY] 3 100-1 4 7,004 =- ‘|) fi'l-Rb - -‘1<>w.-~x.,t’1é‘no a. 65”.) ‘3 Pbfl'HC'W‘ P(K>K)/ rf lalékg/{o D r Mix/~64. «not? 1/9.qu >k 3 3 9000-. M98999») 10mm {mo-MP] = ( m-m gafm-lM-f5L'I 103 d 3 k ., (HI-L) flue-L}? 71F \méksuo } Vast ) m; , i D , 0+Lv’wfsx- E %° t ( . >01) 10 5 b. 1 2 -. '— lol*l01f1031--— I'D w = 5_ i ED‘J ED‘J [2(1)] pm P EM" ‘2: L'f‘xO‘) = E 1“ (wily-401115; - 103.015 knot Luca it:3 ‘ . [[255 ”102.01,: v: ms { __ ,h,,,, .. .._—..H-—‘~~ _‘_:;...-—- --' 5) 5‘07‘53 k. -'\ r _ ?F(‘k‘jqa fill. trial/'1’”. +o Shanpliy mlmmfi ‘H-u. 5|!»an unfit Lem umtmxnoaa k ,__ 5 ‘1 - ' W '1‘ P0531 PO“ 0’1‘ ) gas; K" Pf“), PM L 315,.) 2 if a 2‘1 419 54¢: {0' PW) pug-[J ' Hléfiyzmpy)!’ PO31) u- , k a, _ k g k {HM} g HM . -_ :7 E: )‘ 2: +—1 ILA-b- .—_ -?\ A _ I ulutl. .Sn-MFIL $544., +P(B 190:3] hm: k3- £124; k"- g E3 M .. k w k 1: -)~ °' Is. ‘K -,\ --: -A ,0! "E A A h *3 A (I) _ B Z L-» = a 9.. e. in r 4 "1 ' Q L _‘ - ED —_‘ kzim k. had-J k“ L “I” It K I 1 L E . u, ewkmwh: aiming) _.- .__g._.._Dn_n€__tJ_J¢1&Q __._ _ __.mr.” . 3,, {Lgflgilnwmm ‘9) (5-33“ 0,—er alumna HM gndJ-l's ‘13fi0‘2l5 [um-P; {94.1514} er l‘ {mun W: (: )(za my” D Mag 0; ’4 Mt U. “LL wt} P(‘(=1IK=|) 0.5 i ‘ {1. {M4 Hm dwxi ani c" (KW) . ‘. _ __ . 7 ##7## w.-.,-..~———-7——-*—"’”"_"'“‘ ---—--— -—---‘ ‘1 ' in” "H MW) = F’xtfl‘ My) ‘ ‘0-5' 05W“ “0 Iv” 5:40.05" “‘5 . ; a .95 cm 0,5 . I3‘11.“ “:0 .- ‘1‘", cm. leotuF‘): 0.05 3 o r x M ‘othIo v» A,” P,“ (1.0) 1 ed ‘ ‘ “SW-“H 55H' ‘7“ PxYL‘J‘Jm 0.4 .l' / 0 oflvufvdrk ‘ 1 b! / i Mgh‘du)‘ 1 % rI5 N‘D,| Mft’fiwhkv '7- Pvtfi)‘ 0.55 / ‘ 5 9,0) [2/ me O. 35 \le O oH—WM’SIL .,__.——— ”075'; _ ,, , , fl ....... - ~ 7w ,___-—‘—’" ' _*' " “' ' -... . --——¥..._..r 7-r»--v« 2))1 3- 3'3 2 mt q Hm Pm: 1/3 , 19M Ewan“... 5 1.»)ka P{w)”— Us 3/? ‘1'}: :2 WW knit: Mesa-a '4 blwb ?( $) 1- Ma 3 bails ohm-u Du (hm! K={0,|,13 (W ?={O,‘J1a1§ EL‘, [1N3] 1 a: V2 50‘I'13Pxf (EV) Eixj1 §xpfibc3 may)” PxC“)' Mac) I ra M= gypvcy) mus- § mm) ! mum] = :Zvum Eistm W7] I = Zyl P~f(y)ri[’3(¥,7)i‘f‘y] i ‘- §P~dy3 71:» flur‘m’an) / = $2; 309‘!) by: (by) PM. prmjaepcymv) E[aX‘EY] ’Zééxflby) Path?) PNYO‘IV) ‘* HUTPVLV) /(Ax+5fl : é; ”a; MRI?) 4 53,3; may) émxqu) : my) 7 “ airtight) + bEYPW) K ‘-‘- o‘EDfl * [7E3] clear all; close all; load burgerfry; XY = zeros(6, 4); trials = 10000; for customer : l:trials % sum up all combinations for number of number of burgers and frys that % customers purchased burgers = outcomes(customer, 1); frys = outcomes(customer, 2): XY(burgers, frys) = XY(burgers, frys) + 1; end %compute the probabilities XY = XY/trials; figure(l]; % Loads a new figure bar3(XY); % Plots a 3—dimensional bar graph xlabe1('Fries purchased'); % Places labels on the x—axis of bar graph ylabel('Burgers purchased'}; % Places labels on the y—axis of bar graph zlabel('Probability Mass'}; % Places labels on the z—axis of bar graph View(—100, 50); % Sets the vantage point for viewing the bar graph tit1e('Driginal Joint PMF'); %probability that a customer will buy 3 burgers and two servings of fries is P_3burgers_2fries = XY(3,2) figure(2); % Loads a new figure. marg_fries = sum(XY) % This sums over each column of the matrix KY. marg_burgers = sum(XY'J % This sums over each column of the transposed matrix XY subplottle): bartmarg_burgers); xlabelt'Number of Burgers Purchased'); ylabelt'Probability'); title('Marginal PMF for Burgers Purchased'); subplott212); bartmarg_fries); xlabelt'Servings of Fries Purchased'); ylabelfi'Probability'); V////Eitle('Marginal PMF for Servings of Fries Purchased'); Exp_fries = sum(([1:4].*marg_fries)) Exp_burgers = sum([[l:6}.*marg_burgers)) Exp_Gross_Profit = 2*Exp_burgers+Exp_fries %pmf for burgers a customer will buy given that they buy 2 servings of %fries pmf_burgers_2fries = XY(:,2}/marg_fries(2); figuret3); bar(pmf_burgers_2fries); xlabel('Number of Burgers Purchased'}; ylabelt'Probability'); titlet'PMF for Number of Burgers Buys Given Customer Buys 2 Servings of Fries'); ¢///{;_3burgers_2fries = wmhu‘" h 0 0769 Probabiifil? CHS‘I‘OW Phl‘kw Z bury.“ 3 1 S-Irw'ng: A; {Na—r margufries = 0.2827 0.2632 0.2265 0.2276 margflburgers = h. Probability i=1 0 .D .0 —\ M Li.) J:- D Probability D D 33 .D '—t M m 4:. D [135 0.3 0.25 Probability 0.1 0.05 Marginal PMF for Burgers Purchased 1 2 3 4 Number of Burgers Purchased Marginal PMF for Servings of Fries Purchased 1 2 3 Servings ofFries Purchased 5 PMF for Number of Burgers Buys Given Customer Buys 2 Servings of Fries 3 4 Number of Burgers Purchased 5 Published m‘fl‘! MATLABG 7. 12 ...
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