Smu Assignment Semester – 1

SMU ASSIGNMENT SEMESTER – 1 MB0024

Statistics for Management

SUBMITTED BY:

ANIL KUMAR JOSHI MBA ROLL NO.- 520949950

ASSIGNMENTS- MBA Sem-I MB0024 – Statistics for Management

SET – 1 TABLE –A Months

Monthly Minimum Balance In INR

A/C Holder 2 56000

A/C Holder 3 66000

A/C Holder 4

jan,2008

A/C Holder 1 60000

A/C Holder 6

86000

A/C Holder 5 56000

A/C Holder 8 52000

A/C Holder 9 53000

A/C Holder 10

59000

A/C Holder 7 59000

feb,2008

70000

76000

74000

96000

76000

96000

78000

73000

98000

76000

mar,2008

55000

110000

112000

190000

110000

120000

115000

112000

113000

120000

apr,2008

90000

89000

90000

98000

89000

97000

87000

93000

66000

89000

may,2008

56000

88000

84000

84000

88000

98000

90000

89000

87000

86000

jun,2008

80000

52000

57000

57000

52000

57000

55000

54000

59000

72000

jul,2008

82000

58000

96000

66000

58000

56000

86000

55000

98000

98000

aug,2008

79000

95000

55000

93000

95000

98000

99000

96000

59000

95000

sep,2008

51000

86000

76000

74000

86000

88000

89000

97000

87000

84000

oct,2008

95000

90000

95000

99000

90000

99000

95000

99000

95000

90000

nov,2008

82000

82000

87000

84000

82000

88000

87000

88000

86000

82000

dec,2008

83000

55000

56000

57000

55000

59000

59000

59000

52000

53000

883000

937000

948000

1084000

937000

1015000

999000

967000

953000

1001000

Total

56000


Use the weighted method for solution. 1st a/c holder Wx=883000, W=12 W.Avg.=883000/12 = 73583 2nd a/c holder Wx=937000, W=12 W.Avg.=937000/12 =78083.33 3rd a/c holder Wx=948000, W=12 W.Avg.=948000/12 = 79000 th 4 a/c holder Wx=1084000, W=12 W.Avg.=883000/12 = 90333.33 th 5 a/c holder Wx=937000, W=12 W.Avg.=937000/12 = 78083.33 6th a/c holder Wx=1015000, W=12 W.Avg.=1015000/12 = 84583.33 th 7 a/c holder Wx=999000, W=12 W.Avg.=999000/12 = 83250 8th a/c holder Wx=967000, W=12 W.Avg.=967000/12 = 80583.333 9th a/c holder Wx=953000, W=12 W.Avg.=953000/12 = 79416.667 10th a/c holder Wx=1001000, W=12 W.Avg.=1001000/12 = 83416.667 Table - B A/c Holder

Monthly Avg Balance

1st a/c holder

73583

nd

2 a/c holder

78083

3rd a/c holder

79000 90333 78083 84583

4th a/c holder

5th a/c holder 6th a/c holder

7th a/c holder 8th a/c holder

83250 80583

9th a/c holder

79416

th

10 a/c holder

83416

According to case ABC Branch of XYZ have decided to give 10 Lack of loan each on long term basis to only two of their customer or accountholder, about 20 businessmen had applied for loan in order to develop their business further. In order to reject some of the application as (the fund was limited); the Bank decided that accountholder who had maintained a minimum balance of 50000 INR would only be considered considered for the loan. As a result, result, 10 applications applications were automatically automatically rejected as they were not


satisfying the requirement of minimum balance. Now, the 10 applications remained and it was found that monthly minimum balance in all the cases were more than 50000 INR for the last 12 month. If I was the assistant manager XYZ Bank, I would select from remaining 10 applications and have chosen 4th and 6th A/c Holder for grant of loan because they are only eligible and are maintaining maximum balance than remaining remaining eight A/c Holder’s. Only 4th and 6th A/c Holders have maximum average then others. So, one should should consider grating loan to 4 th and 6th Account holders.


SET-2

1. What What do you mean by sample sample survey? survey? What What are the differen differentt sampling sampling methods? Briefly describe them. Sample is a finite subset of a population drawn from it to estimate the characteristics of the population. Sampling is a tool which enables us to draw conclusions about the characteristics of the population.

describ ribes es the the proc proces esss of sele select ctin ing g a samp sample le of elem elemen ents ts from from a targe targett Survey Survey sampling sampling desc population in order to conduct a survey. A survey may refer to many different types or techniques of observation, but in the context of survey sampling it most often refers to a questionnaire used to measure the characteristics and/or and/or attitudes attitudes of people. people. The purpose purpose of sampling sampling is to reduce the cost and/or the amount of work that it would take to survey the entire target population. A survey that measures the entire target population is called a census. Sample survey can also be described as the technique used to study about a population with the help of a sample. Population is the totality all objects about which the study is proposed. Sample is only a portion of this population, population, which is selected selected using certain statistical statistical principles principles called sampling designs (this is for guaranteeing that a representative sample is obtained for the study). Once the sample decided information will be collected from this sample, which process is called sample survey.

It is incumbent on the researcher to clearly define the target population. There are no strict rules to follow, and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study. Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population. Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn. Sampli Sampling ng method methodss are classi classifie fied d as either either probability or non-probability . In probab probabili ility ty samples, each member of the population has a known non-zero probability of being selected. Proba Probabil bility ity method methodss includ includee random random sampli sampling, ng, systemat systematic ic sampling, sampling, and stratif stratified ied sampling. In non-probability sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In non proba probabil bility ity sampli sampling, ng, the degree degree to which which the sample sample differs differs from from the popula populatio tion n remain remainss unknown. Probability Sampling Methods


1.

Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased. 2. Systematic sampling is often used instead of random sampling. It is also called an N th name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file. 3. Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share share at least least one common common character characterist istic. ic. Exampl Examples es of stratu stratums ms might might be males males and female females, s, or manage managers rs and non-ma non-manag nagers ers.. The The researc researcher her first first identi identifies fies the relevan relevantt stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. " Sufficient " refers to a sample sample size size large large enough enough for us to be reason reasonabl ably y confid confident ent that that the stratu stratum m repres represent entss the population.

Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums. Non Probability Methods 1.

Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensiv inexpensivee approximati approximation on of the truth. As the name implies, implies, the sample sample is selected because they are convenient. This non-probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

2.

Judgment sampling is a common non-probability method. The researcher selects the sample sample based based on judgme judgment. nt. This This is usuall usually y extens extension ion of conven convenien ience ce sampli sampling. ng. For example, a researcher may decide to draw the entire sample from one "representative" city, city, even even thou though gh the the popu popula lati tion on incl includ udes es all all citi cities es.. When When usin using g this this meth method od,, the the researcher must be confident that the chosen sample is truly representative of the entire population.

3.

Quota sampling is the non-probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.

4.

Snowball sampling is a special non-probability method used when the desired sample char charact acter eris istic tic is rare. rare. It may may be extr extrem emel ely y diffi difficu cult lt or cost cost proh prohib ibit itiv ivee to locat locatee respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes comes at the expens expensee of introd introduci ucing ng bias bias becaus becausee the techni technique que itself itself reduce reducess the likelihood that the sample will represent a good cross section from the population.


2. What What is the differe different nt between between correlat correlation ion and regres regressio sion? n? What do you understand by Rank Correlation? When we use rank correlation and when we use Pearsonian Correlation Coefficient? Fit a linear regression line in the following data – X Y

12 123

15 150

18 158

20 27 170 180

34 184

28 176

48 130

Correlation

When two or more variables move in sympathy with other, then they are said to be correlated. corre lated. If both variables move in the same direction then they are said to be positively correlated. If the variables move in opposite direction then they are said to be negatively correlated. If they move haphazardly then there is no correlation between them. Correlation analysis deals with 1) Measuring the relationship between variables. 2) Testing the relationship for its significance. 3) Giving confidence interval for population correlation measure. Regression Regres Regressio sion n is define defined d as, “the “the measur measuree of the average average relati relations onship hip betwee between n two or more more variables in terms of the original units of the data.” Correlation analysis attempts to study the relationship between the two variables x and y. Regression analysis attempts to predict the average x for a given y. In Regression it is attempted to quantify the dependence of one variable on the other. The dependence is expressed in the form of the equations. Different between correlation and regression

Correlation and linear regression are not the same. Consider these differences:  Correlation quantifies the degree to which two variables are related. Correlation does not find a best-fit line (that is regression). You simply are computing a correlation coefficient (r) that tells you how much one variable tends to change when the other one does. 

With correlation you don't have to think about cause and effect. You simply quantify how well two variables relate to each other. With regression, regression, you do have to think about cause and effect as the regression line is determined as the best way to predict Y from X.



With correlation, correlation, it doesn't doesn't matter which of the two variables you call "X" and which you call "Y". You'll get the same correlation coefficient if you swap the two. With linear regression, the decision of which variable you call "X" and which you call "Y" matters a lot, as you'll get a different best-fit line if you swap the two. The line that best predicts Y from X is not the same as the line that predicts X from Y.



Correlation is almost always used when you measure both variables. It rarely is appropriate when one variable is something you experimentally manipulate. With linear regression, the X variable is often something you experimental manipulate (time, concentration...) and the Y variable is something you measure.




The correlation answers the STRENGTH of linear association between paired variables, say X and Y. On the other hand, the regression tells us the FORM of linear association that best predicts Y from the values of X. (2a) Correlation is calculated whenever:

 

 

Both X and Y is measured in each subject and quantifies how much they are linearly associated. In partic particula ularr the Pearso Pearson' n'ss produ product ct moment moment correla correlatio tion n coeffic coefficien ientt is used used when when the assump assumptio tion n of both both X and Y are sample sampled d from from normal normally-d ly-dist istrib ribute uted d popula populatio tions ns are satisfied Or the Spearm Spearman' an'ss moment moment order order correla correlatio tion n coeffi coefficien cientt is used used if the assump assumptio tion n of normality is not satisfied. Correlation is not used when the variables are manipulated, for example, in experiments. (2b) linear regression is used whenever:



 





At least one of the independent variables (Xi's) is to predict the dependent variable Y. Note: Some of the Xi's are dummy variables, i.e. Xi = 0 or 1, which are used to code some nominal variables. If one manipulates the X variable, e.g. in an experiment. Linear regression are not symmetric in terms of X and Y. That is interchanging X and Y will give a different regression model (i.e. X in terms of Y) against the original Y in terms of X. On the other hand, if you interchange variables X and Y in the calculation of correlation coefficient you will get the same value of this correlation coefficient. The "best" linear regression model is obtained by selecting the variables (X's) with at least strong correlation to Y, i.e. >= 0.80 or <= -0.80 The same underlying distribution is assumed for all variables in linear regression. Thus, linear regression will underestimate the correlation of the independent and dependent when they (X's and Y) come from different underlying distributions.

Spearman Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter ρ (rho) or as r s, is a nonparametric measure of correlation – that is, it assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any other assumptions about the particular nature of the relationsh relationship ip between the variables. Certain other measures of correlation correlation are parametric parametric in the sense of being based on possible relationships of a parameterized form, such as a linear relationship. In principle, ρ is simply a special case of the Pearson product-moment coefficient in which two sets of data X i and Y i are converted to rankings xi and yi before before calculating calculating the coefficient. coefficient. In practice, however, a simpler procedure is normally used to calculate ρ. The raw scores are converted to ranks, and the differences d i,i, between the ranks of each observation on the two variables are calculated. If there are no tied ranks, then ρ is given by:


Where: d i = xi − yi = the difference between the ranks of corresponding values X i and Y i, and n = the number of values in each data set (same for both sets). If tied ranks exist, classic Pearson's correlation coefficient between ranks has to be used instead of this formula.

One has to assign the same rank to each of the equal values. It is an average of their positions in the ascending order of the values. Conditions under which P.E can be used : 1. Samples should be drawn from a normal population. 2. The value of “r” must be determined from sample values. 3. Samples must have been selected at random.


3. What What do you you mean mean by bu busi sine ness ss fore foreca cast stin ing? g? Wh What at are are th thee diff differ eren entt methods of business forecasting? Describe the effectiveness of time-series analysis as a mode of business forecasting. Describe the method of moving averages. Business forecasting refers to the analysis of past and present economic conditions with the object of drawing inferences about probable future business conditions. To forecast the future, various various data, information information and facts concerning concerning to economic economic condition of business business for past and present are analyzed. The process of forecasting includes the use of statistical and mathematical methods for long term, short term, medium term or any specific term. Following are the main methods of business forecasting:1. Bu Busi sine ness ss Baro Barome mete ters rs

Business indices are constructed to study and analyze the business activities on the basis of which future conditions are predetermined. As business indices are the indicators of future conditions, so they are also known as “Business Barometers” or “Economic Barometers . With the help of these business barometers the trend of fluctuations in business conditions are made known and by forecasting a decision can be taken relating to the problem. The construction of busin business ess barome barometer ter consis consists ts of gross gross nation national al produc product, t, wholes wholesale ale prices prices,, consum consumer er prices prices,, industrial production, stock prices, bank deposits etc. These quantities may be converted into relatives on a certain base. The relatives so obtained may be weighted and their average be computed. The index thus arrived at in the business barometer. ‟

The business barometers are of three types: i.

ii.

iii.

Barometers relating to general business activities : it is also known as general index of business activity which refers to weighted or composite indices of individual index business activities. With the help of general index of business activity long term trend and cyclical fluctuations in the „economic activities of a country are measured but in some specific cases the long term trends can be different from general trends. These types of index help in formation of country economic policies. Business barometers for specific business or industry : These barometers are used as the supplement of general index of business activity and these are constructed to measure the future variations in a specific business or industry. Business barometers concerning to individual business firm : This type of barometer is constr construct ucted ed to measur measuree the expect expected ed variat variation ionss in a specifi specificc indivi individua duall firm of an industry.

2. Time Series Analysis is also used for the purpose of making business forecasting. The

forecasting forecasting through time series analysis analysis is possible only when the business data of various various years are available which reflects a definite trend and seasonal variation. 3. Extrapolation is the the simp simple lest st meth method od of busi busine ness ss fore forecas casti ting ng.. By extra extrapo pola lati tion on,, a businessman finds out the possible trend of demand of his goods and about their future price trends also. The accuracy of extrapolation depends on two factors: i) Knowledge about the fluctuations of the figures, ii) Knowledge about the course of events relating to the problem under consideration.


4. Regression Analysis The regression approach offers many valuable contributions to the solution of the forecasting problem. It is the means by which we select from among the many possible relationships between variables in a complex economy those which will be useful for forecasting. Regression relationship may involve one predicted or dependent and one independent variables simple regression, or it may involve relationships between the variable to be forecast and several indepe independe ndent nt variab variables les under under multip multiple le regres regressio sions. ns. Statis Statistica ticall techniq techniques ues to estima estimate te the regression equations are often fairly complex and time-consuming but there are many computer programs now available that estimate simple and multiple regressions quickly. 5. Modern Econometric Methods Econometric techniques, which originated in the eighteenth century, have recently gained in popularity for forecasting. The term econometrics refers to the application of mathematical econom economic ic theory theory and statis statistica ticall proced procedure uress to econom economic ic data data in order order to verify verify econom economic ic theorems. Models take the form of a set of simultaneous equations. The value of the constants in such equations is supplied by a study of statistical time series. 6. Expone Exponenti ntial al Smooth Smoothing ing Metho Method d

This method is regarded as the best method of business forecasting as compared to other methods. Exponential smoothing is a special kind of weighted average and is found extremely useful in short-term forecasting of inventories and sales. 7. Choice of a Method of Forecasting The selection of an appropriate method depends on many factors – the context of the forecast, the relevance and availability of historical data, the degree of accuracy desired, the time period for which forecasts are required, the cost benefit of the forecast to the company, and the time available for making the analysis. Effectiveness Effectiveness of Time Series Analysis:

Time Time seri series es anal analys ysis is is also also used used for for the the purp purpos osee of maki making ng busi busine ness ss fore forecas casti ting ng.. The The forecasting through time series analysis is possible only when the business data of various years are available which reflects a definite definite trend and seasonal variation. variation. By time series analysis the long term trend, secular trend, seasonal and cyclical variations are ascertained, analyzed and separated from the data of various years. Merits:

i) It is an easy method of forecasting. ii) By this method a comparative study of variations can be made. iii) Reliable results of forecasting are obtained as this method is based on mathematical model. Method of Moving Averages

One of the most most simple simple and popular popular techni technical cal analys analysis is indica indicator torss is the moving moving averages averages method. This method is known for its flexibility and user-friendliness. This method calculates the average price of the currency or stock over a period of time.


The term “moving average” means that the average moves or follows a certain trend. The aim of this tool is to indicate to the trader if there is a beginning of any new trend or if there is a signal of end to the old trend. Traders use this method, as it is relatively easy to understand the direction of the trends with the help of moving averages. Moving Moving average method is supposed supposed to be the simplest one, as it helps to understand understand the chart patterns patterns in an easier way. Since the currency’s currency’s average price is considered considered,, the price’s volatile movements are evened. This method rules out the daily fluctuation in the prices and helps the trader to go with the right trend, thus ensuring that the trader trades in his own good. We come across different types of moving averages, which are based on the way these averages are computed. Still, the basis of interpretation of averages is similar across all the types. The computation of each type set itself different from other in terms of weightage it lays on the prices of the currencies. Current price trend is always given a higher weightage. The three basic types of moving averages are viz. simple, linear and exponential. A simple moving average is the simplest way to calculate the moving price averages. The historical closing prices over certain time period are added. This sum is divided by the number of instances used in summation. summation. For example, if the moving average is calculated for 15 days, the past 15 historical closing prices are summed up and then divided by 15. This method is effective when the number of prices considered is more, thus enabling the trader to understand the trend and its future direction more effectively. A linear moving average is the less used one out of all. But it solves the problem of equal weightage. The difference between simple average and linear average method is the weightage that is provided to the position position of the prices in the latter. Let’s consider consider the above example. example. In linear average method, the closing price on the 15th day is multiplied by 15, the 14th day closing price by 14 and so on till the 1 st day closing price by 1. These results are totalled and then divided by 15. The exponential moving average method shares some similarity with the linear moving average method. This method lays emphasis on the smoothing factor, there by weighing recent data with higher points than the previous data. This method is more receptive to any a ny market news than the simple average method. Hence this makes exponential method more popular among traders. Moving averages methods help to identify the correct trends and their respective levels of resistance.


4. What What is definiti definition on of Statist Statistics ics? ? What are the differe different nt characteri characteristi stics cs of statistics? What are the different functions of Statistics? What are the limitations of Statistics? According to Croxton and Cowden, ‘Statistics is the science of collection, presentation, analysis and interpretation of numerical data.’ Thus, Statistics contains the tools and techniques required for the collection, presentation, analysis and interpretation of data. This definition is precise and comprehensive. Characteristic of Statistics

a. Statistics Deals with aggregate of facts: Single figure cannot be analyzed. b. Statistics are affected to a marked extent by multiplicity of causes: The statistics statistics of yield of paddy is the result of factors such as fertility of soil, amount of rainfall, quality of seed used, quality and quantity of fertilizer used, etc. c. Statistics Statistics are numerically numerically expressed: expressed: Only numerical facts can be statistically analyzed. Therefore, facts as ‘price decreases with increasing production’ cannot be called statistics. d. Statistics are enumerated or estimated according to reasonable standards of accuracy: The facts should be enumerated (collected from the field) or estimated (computed) with required degree degree of accuracy. The degree degree of accuracy differs from purpose purpose to purpose. purpose. In measuring measuring the length of screws, an accuracy upto a millimetre may be required, whereas, while measuring the heights of students in a class, accuracy upto a centimetre is enough. e. Statistics are collected in a systematic manner: The facts should be collected according to planned and scientific methods. Otherwise, they are likely to be wrong and misleading. f. Statistics are collected for a pre-determined purpose: There must be a definite purpose for collecting facts. Eg. Movement of wholesale price of a commodity g. Statistics are placed in relation to each other: The facts must be placed in such a way that a comparative and analytical study becomes possible. Thus, only related facts which are arranged in logical order can be called statistics. Functions of Statistics

1. It simplifies mass data 2. It makes comparison easier 3. It brings out trends and tendencies in the data 4. It brings out hidden relations between variables. 5. Decision making process becomes easier. Major limitations of Statistics are : 1. Statistics Statistics does does not deal deal with qualitativ qualitativee data. It deals deals only only with quantita quantitative tive data. data. 2. Statis Statistic ticss does does not deal deal with with individu individual al fact: Statisti Statistical cal methods methods can be applie applied d only only to aggregate to facts. 3. Statis Statistica ticall inferen inferences ces (conclu (conclusio sions) ns) are not exact: exact: Statisti Statistical cal inference inferencess are true only on an average. They are probabilistic statements. 4. Statis Statistic ticss can be misused misused and misinter misinterpret preted: ed: Increasi Increasing ng misuse misuse of Statis Statistic ticss has led to increasing distrust in statistics. 5. Common Common men cannot cannot handle handle Statis Statistic ticss proper properly: ly: Only statistici statisticians ans can handle handle statisti statistics cs properly.


5. What are the different stages of planning a statistical survey? Describe the various methods for collecting data in a statistical survey.

The planning stage consists of the following sequence of activities.

1. Nature Nature of the problem problem to be invest investiga igated ted should should be clearly clearly defined defined in an un- ambiguou ambiguouss manner. 2. Object Objective ivess of invest investiga igatio tion n should should be stated stated at the outset. outset. Objectiv Objectives es could be to obtain obtain certa certain in esti estima mates tes or to esta establ blis ish h a theo theory ry or to veri verify fy a exis existi ting ng stat statem emen entt to find find relationship between characteristics etc. 3. The scop scopee of inv investi estiga gati tio on has to be mad made clea clear. r. It refe refers rs to area area to be cove covere red, d, identification of units to be studied, nature of characteristics to be observed, accuracy of measurements, analytical methods, time, cost and other resources required. 4. Whethe Whetherr to use data collecte collected d from primary primary or second secondary ary source source should should be determine determined d in advance. 5. The The orga organi nizat zatio ion n of inve invest stig igat atio ion n is the the fina finall step step in the the proc proces ess. s. It enco encomp mpas asse sess the the determination of number of investigators required, their training, supervision work needed, funds required etc.

Collection of primary data can be done by anyone of the following methods. i. ii.

Dire Direct ct pers person onal al observ servat atio ion n Ind Indirect ora orall in interview

iii. iii.

Info Inform rmat atio ion n thro throug ugh h agen agenci cies es

iv. iv.

Info Inform rmat atio ion n thr throu ough gh mail mailed ed ques questi tion onna nair ires es

v.

Info Inform rmat atio ion n throu through gh sch sched edul ulee fille filled d by inv inves esti tiga gato tors rs


6. What are the the functions functions of classifi classification cation? ? What are the the requisites requisites of a good classification? What is Table and describe the usefulness of a table in mode of presentation of data? The functions of classification are:

a. It red reduc ucee the the bulk bulk data data b. It simplifie simplifiess the data and and makes makes the the data more comprehens comprehensible ible c. It facili facilitate tatess compar compariso ison n of chara character cteristi istics cs d. It renders renders the data ready for any statistical statistical analysis analysis

Requisites of good classification are:

i. ii.

Unam Unambi bigu guou ous: s: It shou should ld not not lead lead to any any con confu fusi sion on Exhaus Exhaustiv tive: e: every every unit unit shoul should d be allott allotted ed to to one one and only only one one class class

iii. iii.

Mutu Mutual ally ly exclu exclusi sive ve:: There There shou should ld not not be be any ove overla rlapp ppin ing. g.

iv.

Flexib Flexibilit ility: y: It shou should ld be be capabl capablee of being being adjust adjusted ed to changi changing ng situ situatio ation. n.

v.

Suit Suitab abil ility ity:: It shou should ld be be suit suitab able le to obj object ectiv ives es of sur surve vey. y.

vi.

Stabil Stability ity:: It should should remain remain stable stable throug throughou houtt the the inve investi stigat gation ion

vii. vii.

Homo Homoge gene neit ity: y: Simi Similar lar uni units ts are are plac placed ed in in the the same same cla class ss..

viii. viii.

Reveal Revealing ing:: Shoul Should d brin bring g out out essen essentia tiall featur features es of of the the colle collected cted data. data.

Table is nothing but logical listing of related data in rows and columns. Objectives of tabulation are:-

a. To simp simpli lify fy comp comple lex x dat dataa b. To highl highligh ightt import important ant charac character terist istics ics c. To pres presen entt data data in mini minimu mum m space space d. To fac facil ilit itate ate comp compar aris ison on e. To brin bring g out out trends trends and tenden tendencies cies f. To facilitate further analysis

Smu Assignment Semester – 1

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