data presentation.ppt

7/24/2019 data presentation.ppt

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Topic: Data Presentation

By the end of this session, the students

should be able to

Define the data

Know the different types of data

Know the different ways to present the

data scientifically and systematically


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Definition of Data

Any observation collected in respect of any

characteristic or event is called data.


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Information

aw data carry!convey little meanin", when it isconsidered alone.

The data is minimi#ed, processed!analy#ed and then

presented systematically. $o that it is converted intoInformation.

%t is important to note that data, that is not converted intoinformation is of little value for evaluation and plannin"

and can not be used by those who are involved indecision ma&in".


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Types of Data

To "ive a holistic picture of classification

data can divided into two types

Quantitative data'numerical(

Qualitative data'descriptive,

cate"orical!fre)uency count(


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Quantitative data

The data , that can be e*pressed in numbers! fi"ures is called)uantitative data. %n this the e*act measures are possible.

%t has two types

(a)Discrete:

Discrete variables can ta&e only certain values and none in

between e. " number of patients in a hospital census may be +- or+, but it cannot be in between these two, similarly the number ofsyrin"es used in a clinic in one day or number of children in a family.

%t is e*pressed in whole number.

(b)Continuous:

/ontinuous variables may ta&e any value 'typically betweencertain limits(. 0or e*ample a"e '12.2 years(, wei"ht '3.2 &"(,hei"ht '+.2 meter( , hemo"lobin '+1.2 "m(, blood pressure '+42!2(.%t can be e*pressed in decimals.


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Qualitative Data

Also called descriptive! cate"orical data!fre)uency count.

5hen the data are arran"ed in cate"ories

on the basis of their )uality and there is"ap between two values, it is called)ualitative data, e." name, reli"ion, maritalstatus, socioeconomic status, awareness.

6ualitative data cannot be e*pressed innumerical forms.


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Types of qualitative data

Nominal data: 7ominal scale data are divided into cate"ories, that are only

distin"uished by their name and labels and cannot be classified oneabove another e." race, name , se*, name of country, name of crops,type of blood. %n this type of data there is no implication of order orratio.

7ominal data that falls into two "roups are called dichotomous datae." male! female, blac&!white, rural! urban.

Ordinal data:

5hen the cate"orical data can be placed in meanin"ful order on thebasis of their )uality, it is &nown as ordinal data. %n this the e*act

difference between the two "roups cannot be estimated e." paincate"ori#ed as mild, moderate and severe. $imilarly scorin" ofstudents cate"ori#ed as A '38 and above(, B '939 8(, / '2328(. %n this the e*act difference between the students placed in"rade A and B cannot be estimated.


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Interval:

%nterval scale data are li&e ordinal data in that they can be placed in a meanin"ful order. The cate"ories are arran"ed in e)ually spaced uni ts and there is no absolute #ero point e." temperature where 3 ; / does not mean no temperature but is e)ual to 41 ; 0 or 14 K ' Kelvin scale (. %n addition they have meanin"ful intervals between items, which are usually measured )uantities. 0or e*ample on the /elsius scal e the difference between +33 ; / and 3 ; / i s the same as the difference between 23 ; / and


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Ratio

A ratio scale has the same properties as an intervalscale> however, because it has an absolute #ero,meanin"ful ratios do e*ist.

?ost biomedical variables form a ratio scale e." wei"ht in"rams or pounds, time in seconds or days, blood

pressure in millimeters of mercury, and pulse rate inbeats per minute are all ratio scale data. The only ratio scale of temperature is the Kelvin scale, in

which #ero de"ree indicates an absolute absence of heat,@ust as a #ero pulse rate indicates an absolute lac& of

heartbeat. Therefore it is correct to say that a pulse rateof +13 beats!min is twice as fast as pulse rate of 93 beats! min, or that 433 K is twice as hot as +23 K.


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Data Presentation

Principals of data presentation

'a( To arran"e the data in such a way that itshould create interest in the readers mind at thefirst si"ht.

'b( To present the information in a compact andconcise form without losin" important details.

'c( To present the data in a simple form so as todraw the conclusion directly by viewin" at the

data. 'd( To present it in such away that it can help in

further statistical analysis.


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Presentation of data

Tabular rap!ical

"imple table comple# table $or quantitative data $or qualitative data

+. =isto"ram +. Bar chart

1. 0re)uency poly"on 1. Picto"ram

4. 0re)uency curve 4. Pie chart


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Tabulation

Tables are the devices, that are used to present the data in a simple form. %t is probablythe first step before the data is used for analysis or interpretation.

eneral principals of desi%nin% tables

a( The tables should be numbered e." table +, table 1 etc.

b( A title must be "iven to each table, which should be brief and self e*planatory.

c( The headin"s of columns or rows should be clear and concise.

d( The data must be presented accordin" to si#e or importance chronolo"ically,alphabetically, or "eo"raphically.

e( %f percenta"es or avera"es are to be compared, they should be placed as close aspossible.

f( 7o table should be too lar"e

"( ?ost of the people find a vertical arran"ement better than a hori#ontal one because, itis easier to scan the data from top to bottom than from left to ri"ht

h( 0oot notes may be "iven, where necessary, providin" e*planatory notes or additionalinformation.

Types of tables

+( $imple tables :?easurements of sin"le set are presented

1( /omple* tables :?easurements of multiple sets are presented


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$imple Table

7ame of country %nfant mortality ratePa&istan 3

Ban"ladesh 93

$ri an&a 19%ndia 93

5hen characteristics with values are presented in the form of

table, it is &nown as simple table e." Table


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$requency distribution table

%n the fre)uency distribution table, the data

is first split up into convenient "roups

'class interval( and the number of items

'fre)uency( which occur in each "roup isshown in ad@acent columns.

=ence it is a table showin" the fre)uency

with which the values are distributed indifferent "roups or classes with some

defined characteristics.


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Rules for construction of

frequency table

+(The class interval should not be too lar"e or too

small

1(The number of classes to be formed more than -

and less than +24(The class interval should be e)ual and uniform

throu"h out the classification.


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Table+.1

rouped, relative, and cumulative fre)uency distributions if

serum cholesterol levels in 133 men

Interval $requency f relative f cumulative f

12+193 2 1.2 +33.3

1


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/harts and Dia"rams

/harts and dia"rams are useful methods of presentin" simple data.

They have powerful impact on ima"ination of people. ives information at a "lance. Dia"rams are better retained in memory than statistical table. =owever "raphs cannot be substituted for statistical table,

because the "raphs cannot have mathematical treatment where astables can be treated mathematically.

5henever "raphs are compared , the difference in the scaleshould be noted.

%t should be remembered that a lot of details and accuracy ofori"inal data is lost in charts and dia"rams, and if we want the realstudy, we have to "o bac& to the ori"inal data.


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/ommon dia"rams

Pie chart $imple bar dia"ram ?ultiple bar dia"ram /omponent bar dia"ram or subdivided bar dia"ram =isto"ram 0re)uency poly"on 0re)uency curve C "ive curve $catter dia"ram ine dia"ram

Picto"ram $tatistical maps


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Bar charts

The data presented is cate"orical Data is presented in the form of rectan"ular bar of e)ual

breadth. Each bar represent one variant !attribute.

$uitable scale should be indicated and scale starts from#ero.

The width of the bar and the "aps between the barsshould be e)ual throu"hout.

The len"th of the bar is proportional to the ma"nitude!

fre)uency of the variable. The bars may be vertical or hori#ontal.


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Bar charts


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?ultiple Bar /harts

Also called compound bar charts ?ore then one subattribute of variable can be

e*pressed


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/omponent bar charts

5hen there are many cate"ories on Fa*is'more than 2( and they have further

subcate"ories, then to accommodate the

cate"ories, the bars may be divided into parts,

each part representin" a certain item andproportional to the ma"nitude of that particular

item


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Component &ar C!art

3

13


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=isto"ram

Gsed for 6uantitative, /ontinuous,

Hariables.

%t is used to present variables which have

no "aps e." a"e, wei"ht, hei"ht, blood

pressure, blood su"ar etc.

%t consist of a series of bloc&s. The class

intervals are "iven alon" hori#ontal a*is

and the fre)uency alon" the vertical a*is.


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0re)uency poly"on

0re)uency poly"on is an area dia"ram of fre)uency distribution over ahisto"ram.

%t is a linear representation of a fre)uency table and histo"ram,obtained by @oinin" the mid points of the hito"ram bloc&s.

0re)uency is plotted at the central point of a "roup


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7ormal fre)uency distribution curve 0re)uency poly"ons may ta&e many different shapes, but

many naturally occurrin" phenomena are appro*imatelydistributed accordin" to the symmetrical, bellshapednormal or aussian distribution.

%n normal distribution curve, the three measures ofcentral tendency are identical. appro*imately 9-8 of thedistributions falls within IJ

+ standard deviation of the mean .

appro*imately 28 of the distributions falls within IJ

1 standard deviation of the mean

appro*imately .8 of the distributions falls within IJ

4 standard deviation of the meanmen and women 'each "ender forms its own distribution

around a different midpoint(.


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Asymmetrical distribution are called s&ewed distributions. The threemeasures of central tendency differ. ?ode is hi"hest point on curve,

the mean is pulled up or down by the influence of a relatively smallnumber of very hi"h or very low scores and the median lies betweenthe two.

n Positively 'or ri"ht( s&ewed distributions and ne"atively 'or left(s&ewed distributions cabe identified by the location of the tail of thecurve.

Positively s&ewed distributions have a relatively lar"e number of lowscores and a small number of very hi"h scores. 7e"atively s&ewed distributions have relatively lar"e number of hi"h

scores and a small number of low scores. Bimodal distributions are sometimes a combination of two

underlyin" normal distributions, such as the hei"hts of a lar"e

number of men and women 'each "ender forms its own distributionaround a different midpoint(.


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ine dia"ram

ine dia"rams are used to show the trend of events with the passa"e of time. ine dia"ram showin" the malaria cases reported throu"hout the word e*cludin" African re"ion

durin" +1-

+3

-

9

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