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Acknowledge
First of all, I would like to say Alhamdulillah, for giving me
the strength and health to do this project work.
Not forgotten my parents for providingeverything, such as money, to uy anything that arerelated to this project work and their advise, which is themost needed for this project. Internet, ooks, computersand all that. !hey also supported me and encouraged meto complete this task so that I will not procrastinate in
doing it.
!hen I would like to thank my teacher, "adam #aiton forguiding me and my friends throughout this project. $ehad some di%culties in doing this task, ut she taught uspatiently until we knew what to do. &he tried and tried toteach us until we understand what we supposed to dowith the project work.
'ast ut not least, my friends who were doing thisproject with me and sharing our ideas. !hey were helpfulthat when we comined and discussed together, we hadthis task done.
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(jective
!he aims of carrying out this project work are)
to apply and adapt a variety of prolem*solvingstrategies tosolve prolems+
to improve thinking skills+
to promote eective mathematical communication+
to develop mathematical knowledge through prolem
solvingIn a way that increases students- interest andcondence+
to use the language of mathematics to e/pressmathematicalideas precisely+
to provide learning environment that stimulates andenhanceseective learning+
to develop positive attitude towards mathematics.
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PA0! (N1
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Introduction
Proaility is a way of e/pressing knowledge or elief that an eventwill occur
or has occurred. In mathematicsthe concept has een given an e/act
meaning in proaility theory, that is used e/tensively in such areas of study
as mathematics, statistics, nance, gamling, science, and philosophyto
draw conclusions aout the likelihood of potential events and the underlying
mechanics of comple/ systems.
Proailityhas a dual aspect) on the one hand the proaility or likelihood of
hypotheses given the evidence for them and on the other hand the ehavior
of stochastic processessuch as the throwing of dice or coins. !he study of
the former is historically older in, for e/ample, the law of evidence, while the
mathematical treatment of dice egan with the work of Pascaland Fermatinthe 2345s.
Proailityis distinguished from statistics. $hile statistics deals with data
and inferences from it, 6stochastic7 proaility deals with the stochastic
6random7 processes which lie ehind data or outcomes.
8I&!(09 * (0I:IN&
Ancient and medieval law of evidencedeveloped a grading of degrees ofproof, proailities, presumptions and half*proofto deal with theuncertainties of evidence in court. In 0enaissancetimes, etting wasdiscussed in terms of odds such as ;ten to one; and maritime insurancepremiums were estimated ased on intuitive risks, ut there was no theoryon how to calculate such odds or premiums.
!he mathematical methods of proaility arose in the correspondence ofPierre de Fermatand 7 on such ?uestions as the fairdivision of the stake in an interrupted game of chance. @hristian 8uygens62347 gave a comprehensive treatment of the suject.
I"P(0!AN@1 (F P0(
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Proaility theory goes eyond mathematics. It involves logic and reasoning
ailities. "arketing and politics have one thing in common, iased statistics. I
elieve since you are e/posed to so many statistics, a asic understanding of
this area allows more critical thinking. !he ook ;8ow to lie with statistics; is
a classic and still in print.
&o, while many people would proaly say that proaility theory has little
importance in their lives, perhaps in some cases if they knew more, it would
have more importance.
8istory of Proaility
Etymology
Probableand likelyand their cognates in other modern languages derivefrom medieval learned 'atinprobabilisand verisimilis, deriving from @iceroand generally applied to an opinion to meanplausibleor generally approved.
Origins
Ancient and medieval law of evidencedeveloped a grading of degrees ofproof, proailities, presumptions and half*proofto deal with theuncertainties of evidence in court. In 0enaissancetimes, etting was
discussed in terms of odds such as ;ten to one; and maritime insurancepremiums were estimated ased on intuitive risks, ut there was no theoryon how to calculate such odds or premiums.
!he mathematical methods of proaility arose in the correspondence ofPierre de Fermatand 7 on such ?uestions as the fairdivision of the stake in an interrupted game of chance. @hristiaan 8uygens62347 gave a comprehensive treatment of the suject.
18th Century
Baco
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the larger the numer of throws, the closer to half*and*half the proportion islikely to e7.
19th Century!he power of proailistic methods in dealing with uncertainty was shown y:aussCs determination of the orit of @eresfrom a few oservations. !hetheory of errorsused the method of least s?uaresto correct error*proneoservations, especially in astronomy, ased on the assumption of a normaldistriutionof errors to determine the most likely true value.
!owards the end of the nineteenth century, a major success of e/planation interms of proailities was the &tatistical mechanicsof 'udwig
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suspended in a Guid. !hat provided a model for the study of randomGuctuations in stock markets, leading to the use of sophisticated proailitymodels in mathematical nance, including such successes as the widely*used
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Applications
!wo major applications of proaility theory in everyday life are in riskassessment and in trade on commodity markets. :overnments typicallyapply proailistic methods in environmental regulationwhere it is called;pathway analysis;, often measuring well*eingusing methods that arestochasticin nature, and choosing projects to undertake ased on statisticalanalyses of their proale eect on the population as a whole.
A good e/ample is the eect of the perceived proaility of any widespread"iddle 1ast conGict on oil prices * which have ripple eects in the economyas a whole. An assessment y a commodity trader that a war is more likelyvs. less likely sends prices up or down, and signals other traders of thatopinion. Accordingly, the proailities are not assessed independently nornecessarily very rationally. !he theory of ehavioral nanceemerged todescrie the eect of such groupthinkon pricing, on policy, and on peaceand conGict.
It can reasonaly e said that the discovery of rigorous methods to assessand comine proaility assessments has had a profound eect on modernsociety. Accordingly, it may e of some importance to most citiens tounderstand how odds and proaility assessments are made, and how theycontriute to reputations and to decisions, especially in a democracy.
Another signicant application of proaility theory in everyday life isreliaility. "any consumer products, such as automoilesand consumerelectronics, utilie reliaility theoryin the design of the product in order toreduce the proaility of failure. !he proaility of failure may e closelyassociated with the productCs warranty.
http://en.wikipedia.org/wiki/Riskhttp://en.wikipedia.org/wiki/Commodity_marketshttp://en.wikipedia.org/wiki/Environmental_regulationhttp://en.wikipedia.org/w/index.php?title=Pathway_analysis&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Pathway_analysis&action=edit&redlink=1http://en.wikipedia.org/wiki/Measuring_well-beinghttp://en.wikipedia.org/wiki/Stochastichttp://en.wikipedia.org/wiki/Behavioral_financehttp://en.wikipedia.org/wiki/Groupthinkhttp://en.wikipedia.org/wiki/Democracyhttp://en.wikipedia.org/wiki/Reliability_theory_of_aging_and_longevityhttp://en.wikipedia.org/wiki/Automobileshttp://en.wikipedia.org/wiki/Reliability_theoryhttp://en.wikipedia.org/wiki/Warrantyhttp://en.wikipedia.org/wiki/Riskhttp://en.wikipedia.org/wiki/Commodity_marketshttp://en.wikipedia.org/wiki/Environmental_regulationhttp://en.wikipedia.org/w/index.php?title=Pathway_analysis&action=edit&redlink=1http://en.wikipedia.org/wiki/Measuring_well-beinghttp://en.wikipedia.org/wiki/Stochastichttp://en.wikipedia.org/wiki/Behavioral_financehttp://en.wikipedia.org/wiki/Groupthinkhttp://en.wikipedia.org/wiki/Democracyhttp://en.wikipedia.org/wiki/Reliability_theory_of_aging_and_longevityhttp://en.wikipedia.org/wiki/Automobileshttp://en.wikipedia.org/wiki/Reliability_theoryhttp://en.wikipedia.org/wiki/Warranty7/26/2019 Kerja Projcj
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!heoretical proaility
Defnition o Theoreti!l "ro#!#ility
Proaility is a likelihood that an event will happen.
$e can nd the theoretical proaility of an event using the following ratio)
'et-s do a couple of e/amples.
$ol%e& E'!m(les on Theoreti!l "ro#!#ility
E'!m(le 1
) *e toss ! !ir oin+ *h!t is the (ro#!#ility th!t ! t!il *ill sho* u(,
$olution-
!ossing a tail is the favorale outcome here.
$hen you toss a coin there are only J possile outcomes) a 8ead or a !ail
&o the options for tossing a tail are 2 out of J.
$e can also represent proaility as a decimal or as a percent.
E'!m(le 2
. #!g ont!ins 20 m!r#les/ 15 o them !re re& !n& 5 o them !re #lue inolor/ in& the (ro#!#ility o (iing ! re& m!r#le/
'et-s rst answer a few ?uestions here)
If I am going to randomly pick a marle from the ag then what results can I have)
I-ll either pick a red marle or a lue one.
"y ne/t ?uestion is what the chances of picking a red marle are)
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!here are 24 red marles and just 4 lue marles.
It-s ovious that we have three times as many red marles as lue marles.
&o, the chance of picking a red marle is more than that of the lue one.
!herefore, the proaility of picking a red marle is)
E'!m(le 3
in& the (ro#!#ility o getting ! sum o 7 *hen you roll t*o &ie/
!wo dice are eing rolled. !he possile outcomes are as follows)
'et-s use the representation 6a, 7 for the outcomes where ! num#er on &ie 1and # num#er on &ie 2.
62, 27, 62, J7, 62, D7, 62, >7, 62, 47, 62, 37,6J, 27, 6J, J7, 6J, D7, 6J, >7, 6J, 47, 6J, 37,6D, 27, 6D, J7, 6D, D7, 6D, >7, 6D, 47, 6D, 37,6>, 27, 6>, J7, 6>, D7, 6>, >7, 6>, 47, 6>, 37,64, 27, 64, J7, 64, D7, 64, >7, 64, 47, 64, 37,63, 27, 63, J7, 63, D7, 63, >7, 63, 47, 63, 37
!here are D3 possile outcomes in all.
The uestion is *hen you roll t*o &ie+ *h!t !re the h!nes o getting !sum o 7,
From the list aove identify the pairs with outcomes that add up to .
'et-s highlight them this way)
62, 27, 62, J7, 62, D7, 62, >7, 62, 47, 1+ 6+6J, 27, 6J, J7, 6J, D7, 6J, >7, 2+ 5+6J, 37,6D, 27, 6D, J7, 6D, D7, 3+ 4+6D, 47, 6D, 37,6>, 27, 6>, J7, 4+ 3, 6>, >7, 6>, 47, 6>, 37,64, 27, 5+ 2, 64, D7, 64, >7, 64, 47, 64, 37,6+ 1, 63, J7, 63, D7, 63, >7, 63, 47, 63, 37
(serve that the pairs along the main diagonal add up to . !here are 3 such pairs.
&o, the proaility of getting a sum of when we roll two dice is)
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1mpirical Proaility
Empirical probability, also known as relative frequency, or experimental probability, is the ratio
of the number favorable outcomes to the total number of trials,[1][2]not in a sample space but in
an actual sequence of experiments. In a more eneral sense, empirical probability estimates
probabilities from experiencean!observation.["]#he phrase a posteriori probability has also been
use! as an alternative to empirical probability or relative frequency.[$]#his unusual usae of the
phrase is not !irectly relate! to %ayesian inferencean! not to be confuse! with its equally
occasional use to refer toposterior probability,which is somethin else.
In statistical terms, the empirical probability is an estimate of a probability. If mo!elin usin a
binomial !istributionis appropriate, it is the maximum likelihoo! estimate.It is the %ayesian
estimatefor the same case if certain assumptions are ma!e for theprior !istributionof the
probability
&n a!vantae of estimatin probabilities usin empirical probabilities is that this proce!ure is
relatively free of assumptions. 'or example, consi!er estimatin the probability amon a
population of men that they satisfy two con!itions( )i* that they are over + feet in heiht )ii* that
they prefer strawberry -am to raspberry -am. & !irect estimate coul! be foun! by countin the
number of men who satisfy both con!itions to ive the empirical probability the combine!
con!ition. &n alternative estimate coul! be foun! by multiplyin the proportion of men who are
over + feet in heiht with the proportion of men who prefer strawberry -am to raspberry -am, but
this estimate relies on the assumption that the two con!itions are statistically in!epen!ent.
& !isa!vantae in usin empirical probabilities arises in estimatin probabilities which are either
very close to ero, or very close to one. In these cases very lare sample sies woul! be nee!e!
in or!er to estimate such probabilities to a oo! stan!ar! of relative accuracy. /ere statistical
mo!elscan help, !epen!in on the context, an! in eneral one can hope that such mo!els woul!
provi!e improvements in accuracy compare! to empirical probabilities, provi!e! that the
assumptions involve! actually !o hol!. 'or example, consi!er estimatin the probability that the
lowest of the !aily0maximum temperatures at a site in 'ebruary in any one year is less ero
!erees elsius. & recor! of such temperatures in past years coul! be use! to estimate this
probability. & mo!el0base! alternative woul! be to select of family ofprobability !istributions
an! fit it to the !ataset contain past yearly values( the fitte! !istribution woul! provi!e an
alternative estimate of the require! probability. #his alternative metho! can provi!e an estimate
of the probability even if all values in the recor! are reater than ero.
http://en.wikipedia.org/wiki/Frequency_(statistics)http://en.wikipedia.org/wiki/Experiencehttp://en.wikipedia.org/wiki/Experiencehttp://en.wikipedia.org/wiki/Observationhttp://en.wikipedia.org/wiki/Observationhttp://en.wikipedia.org/wiki/Bayesian_inferencehttp://en.wikipedia.org/wiki/Posterior_probabilityhttp://en.wikipedia.org/wiki/Posterior_probabilityhttp://en.wikipedia.org/wiki/Binomial_distributionhttp://en.wikipedia.org/wiki/Maximum_likelihood_estimatehttp://en.wikipedia.org/wiki/Maximum_likelihood_estimatehttp://en.wikipedia.org/wiki/Bayesian_estimatehttp://en.wikipedia.org/wiki/Bayesian_estimatehttp://en.wikipedia.org/wiki/Prior_distributionhttp://en.wikipedia.org/wiki/Prior_distributionhttp://en.wikipedia.org/wiki/Prior_distributionhttp://en.wikipedia.org/wiki/Statistically_independenthttp://en.wikipedia.org/wiki/Statistically_independenthttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Probability_distributionshttp://en.wikipedia.org/wiki/Frequency_(statistics)http://en.wikipedia.org/wiki/Experiencehttp://en.wikipedia.org/wiki/Observationhttp://en.wikipedia.org/wiki/Bayesian_inferencehttp://en.wikipedia.org/wiki/Posterior_probabilityhttp://en.wikipedia.org/wiki/Binomial_distributionhttp://en.wikipedia.org/wiki/Maximum_likelihood_estimatehttp://en.wikipedia.org/wiki/Bayesian_estimatehttp://en.wikipedia.org/wiki/Bayesian_estimatehttp://en.wikipedia.org/wiki/Prior_distributionhttp://en.wikipedia.org/wiki/Statistically_independenthttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Probability_distributions7/26/2019 Kerja Projcj
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1mpirical and !heoreticalProaility
1mpirical Proaility of an event is an ;estimate; that the event will happenased on how often the event occurs after collecting data or running ane/periment 6in a large numer of trials7. It is ased specically on directoservations or e/periences.
Em(iri!l "ro#!#ility ormul!
P67 K proaility that an event, , willoccur.TopK numer of ways the specic eventoccurs.!ottom K numer of ways the e/perimentcould occur.
E'!m(le) A survey was conducteddetermine studentsC favorite reeds dogs. 1ach student chose only onereed.
Dog
Collie
$(!niel
!#
o'er
"it
#ull
Other
25 24 D4 E 4 2J
$hat is the proaility that a studenfavorite dog reed is 'aLAnswer) D4 out of the E4 students
chose 'a. !he proaility is
!heoretical Proaility of an event is the numer of ways that the event canoccur, divided y the total numer of outcomes. It is nding the proailityof events that come from a sample space of known e?ually likely outcomes.
Theoreti!l "ro#!#ility ormul!
P67 K proaility that an event, , willoccur.n"#K numer of e?ually likely outcomes
E'!m(le 1) Find the proaility ofrolling a si/ on a fair die.
Answer) !he sample space for rollindie is 3 e?ually likely results) M2, J, D>, 4, 3.!he proaility of rolling a 3 is one o
of 3 or
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of .n"$# K numer of e?ually likely outcomesof sample space $.
E'!m(le 2- Find the proaility of tossing a fair die and getting an odd numer.Answer)event ) tossing an odd numeroutcomes in ) M2, D, 4sample space $) M2, J, D, >, 4, 3
@omparing 1mpirical and
!heoretical Proailities
Haren and Bason roll two dice 45 times andrecord their results in the accompanying chart.1/ $hat is their empirical proaility ofrolling a L2/ $hat is the theoretical proaility ofrolling a L3/ 8ow do the empirical and theoreticalproailities compareL
&um of the rolls oftwo dice
D, 4, 4, >, 3, , , 4,, 25,
2J, , 3, 4, , E, , >,22, 3,
E, E, 25, 3, , >, >, 4,
, ,, , E, 22, 3, 4, >, ,
, >,D, 3, , , , E, 3, ,
E,
$olution-2.7 1mpirical proaility 6e/perimental proaility oroserved proaility7 is 2DO45 K J3.
J.7 !heoretical proaility 6ased upon what ispossile when working with two dice7 K 3OD3 K 2O3K 23. 6check out the tale at the right of possilesums when rolling two dice7.D.7 Haren and Bason rolled more Cs than would ee/pected theoretically.
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PA0! !$(
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a7 "y friends and I are playing "onopoly. At the eginner, each
of us will toss a die once. !he player who otains the highest
numer will start the game. !he possile outcomes are)
M2, J, D, >, 4, 37 Instead of one dice, two dice can also e tossed
simultaneously y each player. !he player will move the
token according to the sum of all dots on oth turned*up
faces. For e/ample, if the two dice are tossed simultaneously
and ;J; appears on one die and ;D; appears on the other,
the outcome of the toss is 6J, D7. 8ence, the player shall
move the token 4 spaces. !he possile outcomes when two
dice are tossed simultaneously)
M62, 27, 62, J7, 62, D7, 62, >7, 62, 47, 62, 37, 6J, 27, 6J, J7, 6J, D7, 6J, >7, 6J, 47, 6J, 37, 6D, 27, 6D, J7, 6D, D7, 6D, >7, 6D, 47, 6D, 37, 6>, 27, 6>, J7, 6>, D7, 6>, >7, 6>, 47, 6>, 37, 64, 27, 64, J7, 64, D7, 64, >7, 64, 47, 64, 37, 63, 27, 63, J7, 63, D7, 63, >7, 63, 47, 63, 37
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PA0! !8011
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!ale 2 show the sum of all dots on oth turned*up faces when
two dice are tossed simultaneously.
&um of dots on
oth turned*upfaces "%#
Possile outcomes Proaility, P"%#
J 62,27 136
D 62,J7,6J,27 236
> 62,D7,6J,J7,6D,27 336
4 62,>7,6J,D7,6D,J7,6>,27 4
36
3 62,47,6J,>7,6D,D7,6>,J7,
64,27
5
36
62,37,6J,47,6D,>7,6>,D7,
64,J7,63,27
6
36
E 6J,37,6D,47,6>,>7,64,D7,
63,J7
5
36
6D,37,6>,47,63,D7,64,>7 4
36
25 6>,37,64,47,63,>7 336
22 64,37,63,47 236
2J 63,37 136
!A
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A K Mthe two numers are not the same
K M62,J7,6J,27,62,D7,6D,27, 62,>7,6J,D7,6D,J7,6>,27,62,47,6J,>7,6>,J7,
64,27, 62,37,6J,47,6D,>7,6>,D7,64,J7,63,27, 6J,37,6D,47,64,D7,
63,J7, 6D,37,6>,47,63,D7,64,>7, 6>,37,63,>7, 64,37,63,47
P 6A7 Kn(A)n(S)
K30
36
< K Mthe product of the two numers is greater than D3
K { }
@ K M7,62,37, 6J,D7,6J,47,6D,J7,6D,>7,6D,37,6>,27,6>,D7,6>,47,
64,J7,64,>7,64,37,63,27,63,D7,63,47
P6@7 K9
36+17
36
K26
36
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Q K M!he sum of the two numers are even and oth numers
are prime7
K M62,27,62,D7,62,47,6J,J7,6J,>7,6J,37,6D,27,6D,D7,6D,47,6>,J7,6>,>7,
6>,37,64,27,64,D7,
64,47,63,J7,63,>7,63,37 M6J,D7,6J,47,6D,J7,6D,D7,6D,47,64,J7,
64,D7,64,47
P 6Q7 K18
36 8
36
K1
9
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PA0! F(R0
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a7 An activity was conducted y tossing two dice
simultaneously 45 times. !he sums of all dots on oth
turned*up faces were oserved. !he fre?uency tale is
completed elow.
&um of the two numers "%# Fre?uency "f#J DD E> 34 3 DE J
>25 >22 D2J 2
!A
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K 7.4596
KJ.D2JJ3ED2
7 New mean value if the numer of tosses is increased to 255times K
c7 !he prediction in 67 is tested y continuing Activity D6a7 until
the total numer of tosses is 255 times. !he value of
6i7 mean+
6ii7 variance+ and
6iii7 standard deviation
of the new data is estimated.
x f fx fx2
J 3 2J J>D J E2> 22 >> 234 2J 35 D55
3 2D E >3E 25 5 >5E 43 >>E 2J 25E J
25 5 5522 3 33 J32J E> 255E
f= 255 fx K34 f x2
3"4"
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* mean K fx
N
K675
100 K 3.4
* SarianceK f x2
N x2
K5363
1006.752
KE.D34
* &tandard deviation K f x
2
N
x2
KJ.EJ33D2DD
!he prediction is proven.
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PA0! FIS1
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$hen two dice are tossed simultaneously, the actual mean and variance of
the sum of all dots on the turned*up faces can e determined y using the
formulae elow)
a7
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"eanK
SarianceK1787
1872
K45.JE
&tandard deviationK 50.277777778
K.553EJ>3J
7
#able below shows the comparison of mean, variance an! stan!ar! !eviation of part $ an! part 3.
5&6# $ 5&6# 3
n37 n177
8ean +.72 +.93 9.77
:ariance 9.$34+ ;."+93 45.JE
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theoretical value. >hen the number of trial )number of sample* ettin bier, the empirical
value shoul! et closer to the theoretical value. /owever, violation of this rule is still possible,
especially when the number of trial )or sample* is not lare enouh.
c7#he rane of the mean
6mean 7
on-ecture( &s the number of toss, n, increases, the mean will et closer to 9. 9 is the theoretical
mean.
Imae below support this con-ecture where we can see that, after 377 toss, the theoretical mean
become very close to the theoretical mean, which is ".3. )#ake note that this is experiment oftossin 1 !ie, but not 2 !ice as what we !o in our experiment*
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FR0!8101TP'(0A!I(N
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'aw of 'arge Numers
In proaility theory, the law of large numers 6''N7 is a theoremthat
descries the result of performing the same e/periment a large numer of
times. According to the law, the averageof the results otained from a large
numer of trials should e close to the e/pected value, and will tend to
ecome closer as more trials are performed.
For e/ample, a single roll of a si/*sided dieproduces one of the numers 2,
J, D, >, 4, 3, each with e?ual proaility. !herefore, the e/pected value of a
single die roll is
1+2+3+4+5+66
=3.5
According to the law of large numers, if a large numer of dice are rolled,
the average of their values 6sometimes called the sample mean7 is likely to
e close to D.4, with the accuracy increasing as more dice are rolled.
&imilarly, when a fair coinis Gipped once, the e/pected value of the numer
of heads is e?ual to one half. !herefore, according to the law of large
numers, the proportion of heads in a large numer of coin Gips should e
roughly one half. In particular, the proportion of heads after nGips will almost
surelyconvergeto one half as napproaches innity.
!hough the proportion of heads 6and tails7 approaches half, almost surelythe
asolute 6nominal7 dierence in the numer of heads and tails will ecome
large as the numer of Gips ecomes large. !hat is, the proaility that the
asolute dierence is a small numer approaches ero as numer of Gips
ecomes large. Also, almost surely the ratio of the asolute dierence to
numer of Gips will approach ero. Intuitively, e/pected asolute dierence
grows, ut at a slower rate than the numer of Gips, as the numer of Gips
grows.
!he ''N is important ecause it ;guarantees; stale long*term results for
random events. For e/ample, while a casino may lose money in a single spin
of the roulettewheel, its earnings will tend towards a predictale percentage
over a large numer of spins. Any winning streak y a player will eventually
e overcome y the parameters of the game. It is important to rememer
http://en.wikipedia.org/wiki/Probability_theoryhttp://en.wikipedia.org/wiki/Theoremhttp://en.wikipedia.org/wiki/Averagehttp://en.wikipedia.org/wiki/Expected_valuehttp://en.wikipedia.org/wiki/Dicehttp://en.wikipedia.org/wiki/Probabilityhttp://en.wikipedia.org/wiki/Sample_meanhttp://en.wikipedia.org/wiki/Fair_coinhttp://en.wikipedia.org/wiki/Almost_surelyhttp://en.wikipedia.org/wiki/Almost_surelyhttp://en.wikipedia.org/wiki/Limit_of_a_sequencehttp://en.wikipedia.org/wiki/Almost_surelyhttp://en.wikipedia.org/wiki/Roulettehttp://en.wikipedia.org/wiki/Probability_theoryhttp://en.wikipedia.org/wiki/Theoremhttp://en.wikipedia.org/wiki/Averagehttp://en.wikipedia.org/wiki/Expected_valuehttp://en.wikipedia.org/wiki/Dicehttp://en.wikipedia.org/wiki/Probabilityhttp://en.wikipedia.org/wiki/Sample_meanhttp://en.wikipedia.org/wiki/Fair_coinhttp://en.wikipedia.org/wiki/Almost_surelyhttp://en.wikipedia.org/wiki/Almost_surelyhttp://en.wikipedia.org/wiki/Limit_of_a_sequencehttp://en.wikipedia.org/wiki/Almost_surelyhttp://en.wikipedia.org/wiki/Roulette7/26/2019 Kerja Projcj
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that the ''N only applies 6as the name indicates7 when a large numberof
oservations are considered. !here is no principle that a small numer of
oservations will converge to the e/pected value or that a streak of one
value will immediately e ;alanced; y the others. &ee the :amlerCs
fallacy.
http://en.wikipedia.org/wiki/Gambler's_fallacyhttp://en.wikipedia.org/wiki/Gambler's_fallacyhttp://en.wikipedia.org/wiki/Gambler's_fallacyhttp://en.wikipedia.org/wiki/Gambler's_fallacy7/26/2019 Kerja Projcj
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01F'1@!I(N
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!1A" $(0H I& I"P(0!AN!
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@(N@'R&I(N
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As a conclusion, now I know+
!he history of proaility from 2Ethcentury to J5th
century,
8ow to apply theory of proaility in daily life and itsimportance,
!wo categories of proaility) empirical proaility
and theoretical proaility and their dierences,8ow to conduct a dice*tossing activity to nd its
proaility,8ow to calculate the proaility,8ow to calculate mean, variance and standard
deviation using formula)
"ean, x fx
N , orx=x P(x)
Sariance, 2= f x2
N x2 , or
x2
P(x )x2
2=
&tandard deviation, =f x
2
N x , or
x2P(x )x2=
Aout 'aw of 'arge Numer 6''N7 and its relation inthis project work,"oral values otained from this project work.
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