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0:00[Music]
0:08[Music]
0:14hello learners i am dr subhani working
0:16with india gandhi national open
0:18university in school of management
0:19studies
0:20and today the topic which i am going to
0:22talk is on uh is on a very contemporary
0:25topic that is graphical representation
0:27we have a very series of discussion
0:29related to data and
0:31how the data is going to you know
0:33represent in the text format
0:35now we are going to talk something more
0:37over and over to that which talks about
0:39that how we are going to
0:41represent the data in a diagrammatical
0:43manner so when we are going to present a
0:45data in a in a visual manner i think
0:49there are two ways by which we can do
0:51the presentation that is the
0:52diagrammatical presentation and the
0:54graphical presentation so already we
0:56have a very very elaborative session
0:58which talks about you know the
0:59diagrammatical presentation where we
1:01have talked about you know the pie chart
1:03the bar chart one dimensional two
1:06dimensional and lot of you know
1:07varieties of ways by which you know the
1:09data is going to be presented in a
1:11diagrammatical manner so this is one of
1:13the sequel to that and here we are going
1:15to talk more about you know the
1:16graphical presentation of data which is
1:18quite meticulous as far as the research
1:21is concerned as far as the statistics is
1:22concerned so anyway
1:25before going into the depth of this
1:26topic i just want to throw a light what
1:29exactly the research methodology and
1:30statistical analysis is you know this is
1:33part of our m com program
1:35and this is a second year course and
1:37exclusively talks about you know the
1:39methodology and the statistical analysis
1:41which we are going to use because in
1:43today's scenario research and statistics
1:45is is a very contemporary term because
1:48with the help of this you know you can
1:50able to visualize many things so already
1:52we have covered somewhere around 13 to
1:5414 lectures if you recalculate yourself
1:57you will find out we have a very
1:59elaborative sessions on research we have
2:00a very elaborate sessions on data
2:02collection
2:03sample measurement of screen techniques
2:05because why am you know just
2:06recapitulating in every session about my
2:08preceding terms because when you talk
2:11about the research when you talk about
2:12the statistics there is a great use of
2:13all these terms
2:15it's not like that that we have used the
2:17term at one step and and you know we are
2:20not going to get the reference of that
2:22but when you are going to
2:24going to the depth of of you know
2:26recapitulating yourself or using the
2:28statistics i think these terms are quite
2:30burdening in nature and have a you know
2:33good presence when you are using in as
2:36far as the methodology is concerned as
2:37far the analysis is concerned so anyway
2:39we have already covered the first block
2:41which was focusing on the research and
2:42data collection and the second block
2:45uh is more talking about you know the
2:47processing of data and how you're going
2:49to preserve the data so while you know
2:51preserving the data or you know the
2:52processor data there are certain modus
2:54operandi which we have to follow and
2:56this diagrammatic presentation is one of
2:58the important ingredients which we have
3:00already covered and talk about how we
3:02are going to present the data in a in a
3:04visual format so already we have a very
3:07innovative session very
3:09thought provoking sessions and now we
3:11are just focusing on the graphic
3:12presentation of data this is
3:14if we go more into the depth of this
3:16graphical representation i think
3:19graphical representation of data is
3:21nothing but a chart in which data is
3:23represented by symbols such as bars in
3:26bar chart lines in line chart or slices
3:28in pie chart so in our preceding session
3:31we have seen that how the diagrammatic
3:32presentation is governed when we are
3:34going to talk about all those aspects
3:36and these are you know converted into
3:38one dimensional two-dimensional
3:40three-dimensional so anyway a data chart
3:42is a type of diagram a graph that
3:44organizes and represents a set of
3:46numerical or qualitative data we have
3:49already talked about quantitative data
3:51and
3:52qualitative data so this data chart is
3:55type of diagram or graph that organizes
3:57and represents a set of numerical or
4:00qualitative data so
4:02in our preceding session i have as i
4:03have talked about we have talked about
4:05bar circles rectangle squares and
4:08certain maps also like flow charts and
4:10other thing
4:11but here we are just concentrating on on
4:13a different kind of diagram so if you
4:16see this particular chapter you know
4:18which is known as unit 7 and the heading
4:20of the chapter is diagrammatic and
4:21graphical presentation which starts with
4:23you know the diagrammatic presentation
4:25and the first you know the ingredient of
4:27this is rules for preparing diagrams
4:29that we have already talked about and
4:31there are certain types of diagrams you
4:33know one dimensional bar diagrams
4:35known as simple bar diagram multiple bar
4:37diagram and subdivided bar diagrams so
4:41already we have talked about it pi
4:42diagrams and then structure diagrams
4:45like you know organizational chart of
4:46flowchart the example which we have
4:48quoted about you know certain
4:49organizations which have a hierarchy of
4:51people starting from your top level
4:53middle level and lower level how this
4:55hierarchy is going to be you know depict
4:57when we are talking in terms of chart so
4:59this organizational chart is very
5:01important then we have a flow chart so
5:02flowchart is working as a decision tree
5:05where you try to do certain things for
5:08if you do that yes is there and if you
5:10know you move on to the different
5:12so certain options are going on so
5:14anyway we have we have covered all those
5:16things now we are just concentrating on
5:18graphical presentation and
5:20this graphical presentation is is the
5:22innovative way of presenting the thing
5:24it could be in the form of ogive in the
5:26form of histogram or you know the
5:28frequency polygons or you know other
5:30ways that we are going to talk about in
5:32a in a coming slides and then we have
5:35graphs of time series which talks about
5:37graphs of one dependent variable and
5:39graphs of more than one dependent
5:40variable so this is a very important
5:42ingredient because
5:44as far as you know the graphs of time
5:45series series is concerned it's it is
5:47just focusing on graphs of one dependent
5:49variable in graphs of more than one
5:51dependent variable then you have graphs
5:54of frequency distribution where
5:55histogram
5:56fall frequency polygon and cumulative
5:58frequencies curves are taken care so
6:01anyway we start with our discussion and
6:04we know that you know the visual
6:05presentation have a great impact
6:07and if you talk in a present scenario i
6:09think we are moving one step ahead to
6:11that and
6:12we are not only you know visualizing the
6:14presentation but also animated in the in
6:16the video format so this is one of the
6:18new ways which are coming in the in the
6:20present circumstances
6:22and this is going to bring a nation the
6:26statistics word or in the research
6:28methodology word so so we have discussed
6:30about one of the techniques of visual
6:31presentation of data that is
6:32diagrammatic presentation
6:34and we appreciate that how such
6:36presentation eliminates the dullness of
6:38data because when you talk about you
6:40know
6:41the
6:42the data analysis in a vis-a-vis you
6:44know the text manner i think somewhere
6:46you know unless is there the monotony is
6:48there so
6:49when you are talking in terms of
6:50presentation in a visual manner it's
6:52more interesting and helps in comparison
6:55between two or more frequency
6:56distributions so this is something which
6:58is which is going to play a very
7:00important role because the role of the
7:01frequency comes in between now we will
7:04study another important technique and
7:06which is known as graphical presentation
7:07and
7:09if we take example of this graphical
7:11representation i think stock index
7:13cricket score production trends that is
7:16in various magazines or in television we
7:18have we have observed that things are
7:19going on and right now you know there
7:20are certain blogs and and
7:23statistical you know with annual reports
7:25which are floating on the website can
7:26also be the example of that so everybody
7:29respect is irrespective of whether he or
7:31she is a layman or an expert has a
7:33natural fascination for
7:35appropriate graphical presentation of
7:37data which remains an essential part of
7:38research mythology i have already quoted
7:40about that the graphical
7:43presentation of data leaves an impact on
7:45the mind of the readers that is very
7:46true because
7:47because when you when you
7:50float certain things through text i
7:51think there are certain monotony and
7:54gigantic due to gigantic shape of data i
7:56think it's really a cumbersome for the
7:58for the for the individual to you know
8:01extract the information from the text so
8:03when you when you make a graph and you
8:05make a pie chart by bar chart or you
8:07know the graphic representation i think
8:09somewhere you know
8:11you are
8:12your your presentation part is quite
8:14good so this graphical representation
8:16encamp has a wide variety of techniques
8:19that are used to clarify interpret and
8:21analyze data by plotting points and
8:23drawing line segments surfaces and other
8:25geometric forms of symbols we are going
8:27to you know talk about all those things
8:29and the purpose of the graph is rapid
8:31visualization of data that is very true
8:33a choice among graphic techniques also
8:35depends on the proposed use to which the
8:37chart will be put into it so these are
8:40the you know the graphical presentation
8:42which is which is only present in the
8:43present scenario that is lawrence curve
8:45histogram frequency polygon frequency
8:48curve and ogive so and all these you
8:50know comes under the embed of called the
8:52graphs of time series why we are coding
8:54the time series because time series the
8:56set of values of a variable or variables
8:59arrange over a period of time so that is
9:01the reason you know we have used the
9:03heading called graphs of time series and
9:06there are certain examples related to
9:07that the data relating to the production
9:09sales expenditure exports
9:11during the last 10 years and the graph
9:14of time series is prepared to show the
9:15values of one or more than one variables
9:17over a period of time so this type of
9:20graphs are also termed as a time graphs
9:22or histograms because history is
9:24represented graphically so that is the
9:26reason you know the time series when we
9:28talk about the time series we talk about
9:29the the past records and the past
9:32history so 10 year down the line we
9:33prepare any chart so
9:35histograms is is known as is one of the
9:38examples to that and these graphs are
9:39helpful in studying the changes over a
9:41period of time and forecasting so on the
9:43basis of the past record and the present
9:45trend we forecast so
9:48this is the beauty of histograms and can
9:50be constructed in two ways on a natural
9:51scale that is arithmetic scale and on a
9:54ratio scale so in natural scale you know
9:56the graph reflects the changes in
9:58absolute values over a period of time
10:00whereas a ratio scale
10:02graph reflects the relative changes over
10:04period time so
10:05in this presentation however we study
10:07the histograms on natural scale which is
10:09generally used in business research
10:11because as far as our paper is concerned
10:13this is
10:14this research mythology paper is for the
10:15masters of commerce student and there's
10:17a great use of you know how the business
10:19trend is changing and how certain new
10:21trends are coming up so this histogram
10:23is going to play a very vital role when
10:25you are going to
10:28work on the time series manner so now
10:30the question is come that when to use
10:32histogram so
10:33we are going to use the histogram when
10:35the data are numerical that is very true
10:37and a lot of mathematics are involved
10:39and you want to
10:41see the shape of the data distribution
10:42especially when determining whether the
10:44output of process is distributed
10:46approximately normally that is that is
10:48required and analyzing analyzing whether
10:51a process can meet the customer
10:53requirements so
10:54that is more important you know if if
10:56the customer requirement or you know the
10:58respondents requirement is not going to
10:59fulfill i think
11:01your analyzing process is not going to
11:03work out so whatever you are going to
11:05analyze is
11:07as you know based on the output for the
11:09supply process looks like and whether
11:11process change has occurred from one
11:12period to another period that also need
11:14to be required if they if there could
11:16not be any change
11:17in the in the in the preceding years or
11:20in the present years i think then there
11:22could not be any competitive view so
11:24determining whether the output or two or
11:26more processes are different so what i
11:29mean to say
11:31we have to communicate the distribution
11:33of data quickly and easily to others so
11:35that is the reason you know there are
11:36certain statistical analysis which need
11:38to talk about all those aspects so when
11:40you are going to talk about the graph of
11:42one dependent variable
11:43when there is only one dependent
11:45variable the values of the dependent
11:46variable are taken on y axis while
11:49the time is taken on x axis so we will
11:51see how the you know this this statement
11:54is going to be interpreted with the help
11:56of certain you know the figures which we
11:58are going to we will study the
12:00certain examples and try to understand
12:02the methods of construction for one
12:03dependent variable histogram so
12:05so and there and there is another term
12:08called false baseline so false baseline
12:09is a device
12:11leading to graphical presentation this
12:12line is used to break the continuity of
12:14y axis with the origin and false
12:17baseline is used when figures start with
12:19high values if we maintain continuity of
12:21the value from the origin then
12:23sufficient portion of the grass would go
12:25waste so what we have observed that this
12:27false baseline is
12:30is working as a device and is related to
12:32the graphical presentation and
12:34this graphs of frequency distribution if
12:36we talk about you know we have seen in
12:38our preceding lecture or in the
12:40presentation that frequency distribution
12:42are you know explained with the help of
12:44tables and these frequency distributions
12:46are also presented in the forms of
12:47graphs and that is you know the the
12:50beauty of because when you talk about
12:52the frequency distribution i think the
12:54data size is quite high and such graphs
12:56can can give a better understanding and
12:59provide illustrative information to
13:00readers
13:01then the data in tabular form so when
13:04you have a data in tablet format i think
13:06the data size is quite high you have
13:07multiple rows multiple columns and
13:09you're not going to interpret what best
13:11can be done but if that if the same
13:13table you know can be governed with the
13:15help of certain charts or certain graphs
13:18i think very easily you can interpret
13:20what what what exactly you are expecting
13:22from that particular image so it is true
13:24that effective graph can
13:26can markly increase the reader's
13:27comprehension of complex data sets
13:30and when you compare with the table
13:31graphs of frequency distribution are
13:32helpful in identifying the
13:34characteristics and relationship of the
13:36data and this relationship is more
13:38important that how the trend is going to
13:40change if we if we take our
13:43you know the chart of 5 to 10 years down
13:45the line very easily we can interpret
13:47that what could be the future of that so
13:49these graphs are useful in locating the
13:51position averages such as mode median
13:53and qualities etcetera we which we have
13:55already covered in measure of center
13:56tendency we have seen that in measure of
13:58center tendency how this
14:02this mean median mode is going to be
14:04taken care so in a continuous frequency
14:06distribution there is a class limit mid
14:08values are taken on y axis or on x axis
14:11and the frequency on the y axis so the
14:14vertical axis which we usually call the
14:16y axis is not broken thus the false base
14:18line cannot be taken so a frequency
14:21distribution if we talk about can be
14:23portrayed by means of histogram
14:25frequency polygon agile curve and
14:27scatter diagram so these scatter
14:29diagrams which we are going to take with
14:31the help of certain examples in our
14:32coming sessions and you will find out
14:34that there is a great use of scattered
14:36diagram but here we are going to start
14:38with certain you know the graphical
14:40presentations and histogram is one of
14:42that
14:43and it's it's part of the time series
14:44and histogram is an approximate
14:46representation of the distribution of
14:48numerical data it was first introduced
14:50by karl pearson that is
14:52and and these frequency distributions
14:54shows how often each different value in
14:57set of data occurs
14:59and histogram is the most commonly used
15:00graph to show frequency distributions we
15:02will when we will take certain examples
15:05we see that how it is quite important it
15:07look very much like a bar chart but they
15:09are important difference between them
15:11because when we talk about the bar chart
15:13there's a gap between two bars but when
15:15you talk about the histograms histograms
15:17are
15:19are you know
15:20linked with one another the bars are
15:22attached with one another without any
15:24gap so when we will take certain
15:26examples we will see how important it is
15:28so histogram meaning can be stated as a
15:30graphical representation
15:32that condenses that data series into an
15:35easy interpretation of numerical data by
15:37group grouping them into logical ranges
15:39of different heights which are also
15:41known as bins
15:42and a histogram is used to display
15:44continuous data in a categorical form so
15:47this is a usp of of this histogram and
15:52and if we if we talk about how to create
15:55a histogram that is something because
15:57there are certain parameters which one
15:59has to follow and if we go more into the
16:01depth of it you will find out that how
16:03the things are going to be calculated so
16:04if we are going to create a histogram i
16:06think to construct a histogram the first
16:09step is to
16:11bin or bucket the range of values that
16:13is divide the entire range of values
16:15into a series of intervals and then
16:17count how many values fall in each
16:19interval the the bins are usually
16:21specified as consecutive non-overlapping
16:23intervals of a variable so
16:26we have we have seen that you know for
16:28two you know the two is the score and
16:30the frequency is three
16:32three is the score frequency is seven 7
16:364 is the score frequency is 2 so 6 is
16:39the score frequency is
16:415. so this is the in this manner you
16:44know we have we have plotted the
16:46histograms and you see what is the
16:48impression behind that that
16:50that you know the initially we are
16:53gradually we are going up then going
16:54down then after certain time then again
16:57go up and then go down so this is the
16:59model so there are certain facts and
17:00figures related to histogram
17:02now the question is that is histogram is
17:04a qualitative or quantitative pie charts
17:07and bar diagrams are used for
17:08qualitative data that is very true we
17:10have already seen that
17:12and histograms similar to bar graphs are
17:14used to
17:16for the quantitative data line graphs
17:18are used for qualitative data scatter
17:20graphs are used for quantitative data so
17:23if you talk about you know this bar
17:25chart and pie chart they are more
17:27qualitative in nature where histogram is
17:28more quantitative quantitative in nature
17:31so how to interpret the shape of the
17:33statistical data in a histogram
17:35we have seen that you know this
17:37symmetric is there then skewed light is
17:39there and
17:41and when we go more into depth of
17:43symmetric i think the histogram is a
17:45symmetric if you cut it down
17:47the middle in the left hand and right
17:48hand side resemble mirror image of each
17:50other that is very true and skewed right
17:53histogram looks like a lopsided mound
17:55with a tail going off to the right
17:57so and
17:59there are certain examples of the skewed
18:00left also so what does the shape of a
18:02histogram tell us so it's a bi model we
18:05have already talked about you know in
18:07in in mode when we are going for the
18:09mode we have seen that how the bi model
18:11and multi model works the bi model shape
18:13shown below has two peaks if this shape
18:16occurs the two sources should be
18:18separated and analyzed separately
18:20and this is the manner and and some
18:22histograms will show a squee
18:24distribution to the right as shown in
18:26below and what we have seen that you
18:28know if you see this particular image
18:30you will find out that that how the
18:32histogram the rivals are there the
18:33frequency is at the at one axis and i
18:36was at
18:37different and then we have plotted the
18:39histograms so now there this is another
18:42way of explaining the histogram
18:43distribution of randomly generated
18:45numbers
18:46where we have seen that how the
18:47histogram is going to create it but
18:49the good part of the histogram is that
18:52that all the bars are are
18:54attached with one another there is no
18:55gap between the two bars whereas in
18:58when bar chart you will find out that
19:00there is a gap between the two so this
19:03is the difference between the histograms
19:04and bar chart and you see that how the
19:06gaps no gaps are there whereas you know
19:09in bar chart you will find out the gaps
19:10are
19:11there so anyway now we are going to talk
19:13about another types of
19:16graphical
19:17presentation that is frequency polygon
19:20and when you go more into depth of
19:22polygon i think its many angle diagrams
19:25so this is another way of defecting a
19:26frequency distribution graphically
19:29it facilitates comparison of two or more
19:31frequency distributions so frequency
19:34polygon can be drawn either from the
19:36histogram or from the given data
19:38directly so you have got varieties of
19:40way and if you see this particular image
19:42you will find out that that histogram is
19:45is this and frequency polygon is is this
19:48that in the same
19:49in first we have plotted the histogram
19:52and gradually after plotting the
19:53histogram we have taken the midpoint of
19:55each bars and then plot a frequency
19:58polygon so this this bar is nothing but
20:00a histogram which is attached with one
20:02another whereas on the whereas frequency
20:04polygon is just the midpoint of that bar
20:07and and dot is the representation and
20:10gradually we have seen that how we have
20:12plotted this so
20:13ah so what we what we keen to say that
20:16when we talk about the cumulative
20:17frequency curves sometimes we are
20:20interested in knowing how many families
20:21are there in the city or we can take
20:23certain examples whose earnings are less
20:25than twenty thousand per month who's
20:27earning a more than you know thirty
20:28thousand per month so in order to obtain
20:30this information we have
20:32first of all to convert the ordinary
20:34frequency table into cumulative
20:36frequencies when cumulative frequency is
20:38going to be added you know with the help
20:40of certain numbers which can be added so
20:43if
20:44the if in the
20:46table we have got two
20:48ways and frequency is general so
20:50cumulative frequency is the addition of
20:52the existing one and the preceding one
20:54so we add we get the cumulative so next
20:56could be added of that
20:57so when the frequency is added they are
20:59called cumulative frequencies and the
21:01curves obtained from the cumulative
21:02frequencies are called cumulative
21:04frequency curves properly known as
21:06ogives so there are two types of ogives
21:08we have
21:10we are going to talk about in a more
21:11elaborative manner and if you if you see
21:14this so these ogives are are you know
21:18start with the upper limit of each class
21:20and the cumulative starts from the top
21:22so when these frequencies are plotted we
21:23get less than oh so they are less than
21:26aji and they are more than ogi so
21:29and if you go more into depth of the ogi
21:31these objects are useful to determine
21:33the number of items a number below a
21:36given value it is also useful for
21:37comparison between two or more frequency
21:39distributions and to determine certain
21:42values like you know the positional
21:44values such as mode
21:46median quartile percentile etcetera
21:48because we have we have seen that that
21:51that when you talk about the measure of
21:53central tendency this mode median r
21:55seems to be the positional values
21:58because with the help of there is
22:00no
22:01you know impact of the outliers and if
22:03the when you talk about the mode the
22:05number which repeated maximum number of
22:07times we have already have a very
22:08elaborative session on measure of
22:09central tendency
22:11which are considered to be the mode and
22:13medium is the middle number so so what
22:15we have observed that you know an ogi
22:17sometimes called a cumulative frequency
22:19polygon is a type of frequency polygon
22:22that shows cumulative frequencies so in
22:25other words the cumulative percents are
22:27added on the on the graph from the left
22:29to right and ogi graph plots cumulative
22:31frequencies on the y axis and class
22:33boundaries along the x axis so the hive
22:37for the normal distribution resembles
22:39one side of an arabesque or ogivel arc
22:43which is
22:44likely the origin of its name so if you
22:46see this particular image you will find
22:48out that how we have plotted the odais
22:51and this is the manner you know the guys
22:53are going to be
22:56developed in a particular image so
22:58there are certain differentiation
23:00between less than and more than agile so
23:02in this objective you know the
23:03frequencies are added starting from the
23:05upper limit of the first class interval
23:07of the frequency distribution and
23:10in this agile the cumulative total tens
23:12is going to be in a screen increase mode
23:14and when you are going to talk about you
23:16know the more elaborate manner about the
23:18lesser know drive the plots the points
23:20with the upper limits of the class as
23:23abscissa and the corresponding less than
23:25cumulative frequencies as ordinates
23:28so
23:29these points are joined by freehand
23:31smooth curve to give less than
23:32cumulative frequency curve or the less
23:34than the ogive so this is example of
23:37less than oji and when you go and talk
23:40more about the more than typogy it is a
23:42graph drawn from lower limits and
23:44cumulative frequencies of a distribution
23:46and when we are going to mark the points
23:48with the lower limit and x coordinate
23:50and corresponding cumulative frequency
23:53and y coordinate and gen
23:55and then when we are going to join them
23:56by freehand smooth curve this type of
23:58graph is accumulated as downward so
24:01what we have observed that you know this
24:03ai is also quite important and uh when
24:07and there are certain you know
24:09the time when when we are making a
24:11graphical presentation this histograms
24:13and ojai is very important but on the
24:14other hand the lawrence curve is also
24:16one of the way of graphical
24:19representation of income equality of
24:21wealth equality
24:22and this was developed by the american
24:24economist called max lawrence in
24:261905 and the graph plots percentile of
24:29the population on the horizontal axis
24:31according to the income of wealth so
24:33what we have seen from this particular
24:35image that that we bifurcate this whole
24:38whole rectangular box and from the from
24:41the mid from 0.0 and then you know this
24:44considered to be the line of equality
24:45and orange curve supposed to be moving
24:48that way so this red line is considered
24:50to be the lorenz curve and if you talk
24:52more about the lorenz card the lorenz
24:53curve is a graphical method used to
24:55display the concentration of activities
24:57within an area example given the degree
25:00of industrial specialization within an
25:02urban area feed work data may be used
25:04but it is more common to use secondary
25:06source
25:07and the technique is particularly useful
25:09as it provides a good visual comparison
25:10of any observed difference and from its
25:13precise index
25:14which genogenic coefficient can be
25:16calculated so the further away you know
25:19lawrence curve is from the line of
25:21perfect equality which is diagonal we
25:23have seen that the more diverse is the
25:25sample and the more unevenly the values
25:27are spread out so this is useful to
25:29estimate how wealth is distributed among
25:31the population so if a country's
25:33lawrence curve is distant from the line
25:35of perfect equality it means a small
25:37percentage of the population controls
25:39most of the world and that country
25:41income distribution is uneven i think we
25:44have we have talked a lot about
25:46graphical representation and we have
25:48seen that how there is a difference
25:49between the diagrammatical presentation
25:51and graphical representation so
25:54this is one of the important ingredient
25:56as far as you know the presentation of
25:57data is concerned and one of the one of
26:00the important component of of the block
26:022 which talks about you know how you are
26:04going to process the data because
26:06we know that the data is quite gigantic
26:08in nature and it's really cumbersome for
26:10the for the for the researchers to
26:12accumulate the data or get the results
26:14out of data out from that data because
26:16if the respondents have reciprocates and
26:18they have given the
26:20information in a tabulated format so
26:23it's really you know tough for the
26:24researchers to go for that so in that
26:26case if they use this statistical
26:28phenomena or you know the way of
26:30presenting the data i think this is
26:32somewhere where they can get the
26:35you know depict the things in a very
26:36faster mode so graphical presentation is
26:39very important ingredient and
26:41as far as this particular block is
26:43concerned which is more emphasize on
26:45processing and preservation of data this
26:47graphical presentation diagrammatical
26:49presentation and processing of data is
26:51important in gradient because the first
26:53block the the very first block consists
26:55of you know somewhere around 10 to 12
26:58lectures and have
26:59was was focusing on you know how you are
27:01going to do the research do the analysis
27:03and collect the data
27:05but now this this the second block is
27:07more emphasizing on preservation of data
27:09because if you preserve the data and
27:11interpret the results i think somewhere
27:13you know
27:14you can able to move in a more
27:17systematic manner so
27:19we have some more sessions you know
27:20which talk something related to
27:23processing and preservation of data in
27:25our forthcoming session thank you very
27:27much
27:30[Music]
27:43you

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