Basic Math 2010
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Transcript of Basic Math 2010
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BASIC MATHEMATICS [S T A T I S T I C S]
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BASIC MATHEMATICS [S T A T I S T I C S]
CONFESSION PAGE
We recognize this work is the result of our own except for quotation and a summary of
each of them we describe the source
Signature : ...................
Name : Che Nurul Azieana Binti Che Yang
Date : 16th April 2010 .
Signature : ...................................... ............
Name : Nor Atirah binti Mohd Rapingi
Date : 16th April 2010.
Signature : ...................................... .....
Name : Madhihah binti Nordin
Date : 16th April 2010.
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BASIC MATHEMATICS [S T A T I S T I C S]
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BASIC MATHEMATICS [S T A T I S T I C S]
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BASIC MATH
MATICS [S T A T I S T I C S]
N A E : HENURUL AZIEAN A BT HE YANG
I. .NUMBER : 9 7- -558
ATE OF BIRTH : 7TH E EMBER 99
PLA E OF BIRTH : KLUANG, JOHOR
A RESS : NO 4 , JLN SRI ANGI, TMN SULIANA, SIKAMAT 7 4
SEREMBAN,NEGERI SEMBILAN.
GROUP : PPISMP (M ATH )
TEL.NO : 3- 9 93
HOBBY : LISTENING TO MUSI
AMBITION : LE TURER
E UCATION : SEK.KEB.TAMAN PAROI JAYA,
SEK.MEN.KEB. ATO HJ.MOH RE ZA,
SEK.MEN.TEKNIK AMPANGAN
JOHORMATRICULATIONCOLLEGE
FATHERS NAME : CHE YANG BIN HJ.SAMSUDIN
OCCUPATION : RETIRED SOLDIER
MOTHERS NAME : NORMALA BT ZAINOL ABIDIN
OCCUPATION : HOUSE IFE
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BASIC MATHMATICS [S T A T I S T I C S]
NAME : NOR ATIRAH BINTI MOHDRAPINGI
I.C.NUMBER : 9 5 - -5 44
DATE OF BIRTH :TH
M AY 99
PLACE OF BIRTH : LANGKA I, KEDAH
ADDRESS : PS 4 , KAMPUNG PADANG KANDANG,MKM PADANGMATSIRAT,
7 ,LANGKA I, KEDAH
GROUP : PPISMP (M ATH )
TEL.NO : 3- 3 494
HOBBY : ARCHERY
AMBITION : LECTURER
EDUCATION : SEK.KEB.KUALA TERIANG
SEK.MEN. AGAMA PERSEKUTUAN KAJANG
KUALA NERANGMARA MATRICULATIONCOLLEGE
UNIVERSITY MALAYA
FATHERS NAME : MOHDRAPINGI BIN YUSOF
OCCUPATION : TEACHER
MOTHERS NAME : HAPISHAH BINTI YOM
OCCUPATION : HOUSE IFE
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BASIC MATHMATICS [S T A T I S T I C S]
NAME : MADHIHAH BINTI NORDIN
I.C.NUMBER : 9 9 - 4- 3 8
DATE OF BIRTH :
t SEPTEMBER 99
PLACE OF BIRTH : KLUANG, JOHOR
ADDRESS : G- - , QUARTERS PERKHIDMATAN A AM,NO , JALAN
DUTAMAS 3, 5 48 ,KUALA LUMPUR.
GROUP : PPISMP (M ATH )
TEL.NO : 7- 3 5
HOBBY : LISTENING TO MUSIC
AMBITION : LECTURER
EDUCATION : SEK RENCONVENT SENTUL
SEKMENCONVENT
SEKMEN SAINS SERI PUTERI
FATHERS NAME : NORDIN BIN YUSOF
OCCUPATION : PUBLIC ADMINISTRATOR
MOTHERS NAME : NOOR AZIZAH BINTI AHMAD
OCCUPATION : CUSTOMER SERVICE OFFICER IMR
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BASIC MATHEMATICS [S T A T I S T I C S]
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BASIC MATHEMATICS [S T A T I S T I C S]CONTENT
BIL CONTENT PAGES
CONFESSION PAGE
QUESTION
PROFILE
CONTENT
ACKNOWLEDGEMENT
INTRODUCTION
ANALYSIS
CONCLUSION
COLLABORATION FORM
APPENDICES
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BASIC MATHEMATICS [S T A T I S T I C S]
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BASIC MATHEMATICS [S T A T I S T I C S]
ACKNOWLEDGEMENT
irst of all, we would like to thank God that after all the hardship that we need to face
up; we manage to complete the assignment in the time given by the topic of matrices.
We are really appreciated those who lend their hand, give fantastic idea and share
their fabulous opinion especially Madam Norehan, our Mathematics lecturer that always
guide us in order to complete this assignment. She always makes sure all of us are
understand what the task craved.
Besides that, we also would like to thank our precious parents and families that
always are with us in hardship or happy time and always support a nd give advice to ensure
all of us are not give up although there are many obstacles.
Credits also for our beloved friends that let us share the information and help each
other to make sure all of us made assignment that follow the instructions. Not forgo tten
each our group members that always give full commitment and cooperation.
So, we are really appreciating all the effort those who help us whether their name was
mentioned or not.
hank you.
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BASIC MATHMATICS [S T A T I S T I C S]
as a means of understanding complex social phenomena such as
crime rates,marriage rates,orsuicide rates.Charles S. Peirce ( 839--
9 4) f ormulated frequenters theories of estimation and hypothesis-
testing in ( 877-- 878) and ( 883), in which he introduced
"confidence". Peirce also introduced blinded, controlled randomi ed
experiments with a repeated measures design. Peirce invented an
optimal design forexperimentsongravity.
Thewordstatisticscaneitherbesingularorplural. In itssingular form,astatistic isa
quantity (such as a mean) calculated from a set of data, whereas statistics is the
mathematical sciencediscussed in thisarticle. Statisticsalways related to thegraph. Forexample,bargraph,histogram,piechart, frequencypolygonandhistogram.
In statistics, a histogram is agraphical display of tabular frequencies, shown as
adjacent rectangles. Each rectangle iserectedoveran interval,with anareaequal to the
frequencyof the interval. Theheight ofarectangle isalsoequal to the frequencydensityof
the interval, i.e. the frequencydividedby thewidth of the interval. The total areaof the
histogram isequal to thenumberofdata. A histogrammayalsobebasedon the relative
frequencies instead. It then shows what proportion of cases fall into each of several
categories (a formofdatabinning),and the total area thenequals . Thecategoriesare
usually specified as consecutive, non-overlapping intervals of some variable. The
categories (intervals) must beadjacent,andoftenarechosen tobeof thesamesi e, [1]but
not necessarilyso.
Histograms are used to plot density of data, and often for density estimation:
estimating the probabilitydensity functionof theunderlying variable. The total areaof a
histogram used for probability density is always normali ed to 1. If the lengths of the
intervalson thex-axisareall 1, thenahistogram is identical toa relative frequencyplot.
Analternative to thehistogram is kernel densityestimation,whichusesakernel tosmooth
samples. Thiswill construct asmoothprobabilitydensity function,whichwill ingeneral more
accuratelyreflect theunderlying variable. Thehistogram isoneof thesevenbasic toolsof
qualitycontrol.
Charles S. Peirce
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BASIC MATHMATICS [S T A T I S T I C S]
A barchart orbargraph isachart withrectangularbarswith lengthsproportional
to thevalues that theyrepresent. Thebarscanalsobeplottedhori ontally.
Barchartsareused forplottingdiscrete (or 'discontinuous') data i.e. datawhichhas
discretevaluesand isnot continuous. Someexamplesofdiscontinuousdata include 'shoe
si e' or 'eyecolour', forwhichyouwoulduseabarchart. Incontrast,someexamplesof
continuousdatawouldbe 'height' or'weight'. A barchart isveryuseful ifyouare t rying to
recordcertain informationwhetherit iscontinuousornot continuousdata.
DIAGRAM 1.0 : EXAMPLE OF HISTOGRAM
DIAGRAM 1.1 : EXAMPLE OF BAR CHART
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BASIC MATHMATICS [S T A T I S T I C S]
A pie chart (or a circle graph) is circular chart divided into sectors, illustrating
proportion. Inapiechart, the arc lengthofeachsector (andconsequently itscentral angle
andarea), isproportional to thequantity it represents. Together, thesectorscreatea full
disk. It isnamed for itsresemblance toapiewhichhasbeensliced. Theearliest knownpie
chart isgenerallycredited to illiam Playfair'sStatistical Breviaryof18 1.
Thepiechart isperhaps themost ubiquitousstatistical chart in thebusinessworld
and themassmedia. It canalsohelppeopledowork. However, it hasbeencritici ed,and
somerecommendavoiding it,pointingout inparticular that it isdifficult tocomparedifferent
sectionsofagivenpiechart,or tocomparedataacrossdifferent piecharts. Piechartscan
beaneffectivewayofdisplaying information insomecases, inparticular if the intent is to
compare the si eof a slicewith thewhole pie, rather than comparing the slicesamong
them. Piechartsworkparticularlywell when theslicesrepresent 5 to 5 % of thedata,but
ingeneral,otherplotssuchas thebarchart or thedot plot,ornon-graphical methodssuch
as tables,maybemoreadapted forrepresentingcertain information.
DIAGRAM 1.2 : EXAMPLE OF PIE CHART
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BASIC MATHEMATICS [S T A T I S T I C S]
CONCLUSION
Statistics is the science of making effective use of numerical data relating to groups
of individuals or experiments. It deals with all aspects of this, including not only the
collection, analysis and interpretation of such data, but also the planning of the collection of
data, in terms of the design of surveys and experiments. In this assignment, the data
collection is about the siblings among 0 students out of whole students of one of the
college.
rom this data collected, there is represented using bar graph, histogram,
frequency polygon and pie chart. he data was represented using these graphs to make it
easier to read by the statistician. Statistician will get many data when the data is
representing using visual representative. ne of them is mode. Mode is the higher number
of the data. When the data is representing using bar graph or histogram , it is very clear and
easier to know the mode. ust take the higher one. ther than that, visual representative
also can help statistician easy to find mean and median.
In the other hand, by using data representative we can get a lot of information from
the data. We will get the highest, the higher, the lower a nd the lowest number of the data
that we are collect. rom the graph also we will get doubled or triple data rather than just
using tabled representation or raw data. he information we are collected from the visual
representative are stayed at the page in front in analysis.
Last but not least, we want to highlight that visual representative is very important in
statistics. his is because a list of raw data may be difficult to interpret; psychologists prefer
to represent their data in an organized way. wo of the most common ways are frequency
distributions and graphs. here are many types of visual representative of data in statistics
such as histogram, bar chart, pie chart, line graph, frequency polygon and others. So,choose what is suitable for your data and make it easier.