1. BY ANITHA.C ASHA V DEEPTHI.J SHALINI

2. OBJECTIVE: The main objective of our project is collection, classification, analysis and interpretation of data for making effective decisions and to show an understanding of the basic concepts of Statistics. DATA COLLECTION: The data base includes 28 Global out sourcing companies from both India and abroad. The data was collected from www.sourcingmag.com NASSCOM site and individual web sites of the company. VARIABLES The variables used are Name of Companies, Services and Location. The other variables are Revenue, Net profit Margin, Net Profit and No of Employees.

3. NAME OF COMPANIES Revenue (M) Net Profit Margin Net Profit (M) EmployeesServicesLocation Cognizant88618.34162.4925000BothFOREIGN Perot Systems 20005.20104.0018000ITOFOREIGN Infosys220026.02572.4458000BothINDIA TATA Consulting Services 290021.97637.1354000ITOINDIA HCL7572.5719.4513000ITOINDIA Genpact100012.25122.5030000BPOINDIA Mellon430018.88811.8417000BPOFOREIGN Hewlett Packard 867004.073528.69150000ITOFOREIGN Convergys26004.89127.1466000BPOFOREIGN Capgemini69002.03140.0760000BothFOREIGN i-Flex Solutions 32316.6553.786000ITOINDIA Larsen & Toubro InfoTech 21008.32174.7224000ITOINDIA Fiserv406011.66473.4023000BPOFOREIGN Accenture171007.181227.78123000BothFOREIGN Ceridian14609.57139.659000BPOFOREIGN ICICI One Source 1238.9911.068500BPOFOREIGN Satyam110022.81250.9129000BothINDIA IBM Global Services 462009.334310.4620000BothFOREIGN Oracle1180023.512774.1850000ITOFOREIGN Datamatics3014.844.502000BPOFOREIGN EDS197001.52299.44117000BothFOREIGN Wipro230018.95435.8555000BothINDIA ADP850012.381052.3044000BPOFOREIGN Computer Sciences Corp 146003.95576.7079000ITOFOREIGN Xansa3763.6713.806000BPOFOREIGN MphasiS20515.9432.6812000BothFOREIGN Peoplesupport6232.2720.044000BPOFOREIGN Hewitt29004.76138.0422000BPOFOREIGN

4. Revenue: For a company, this is the total amount of money received by the company for goods sold or services provided during a certain time period. Net Profit: It shows what the company has earned (or lost) in a given period of time. Net Profit Margin: It is the net profit divided by net revenue, often expressed as a percentage. The higher the net profit margin is, the more effective the company is at converting revenue into actual profit. Employee: It is the total employee strength of the firm. Location: Location of the company’s head quarter.

5. DATA TYPES Qualitative data: Data are non numeric in nature and can’t be measured. Here services and location of outsourcing companies are the qualitative data. Quantitative data: Data are numerical in nature and can be measured. Here revenue, net profit, net profit margin and employees are taken as quantitative data.

6. QUALITATIVE DATA ANALYSIS INDIAN 29% Foreign 71% INDIAN Foreign The pie chart shows that most of the companies are foreign companies compared to the Indian companies. Out of the 28 out sourcing companies 71% are foreign and 29% are Indian companies.

7. b) DISTRIBUTION OF COMPANIES BASED ON SERVICES PROVIDED BPO 39% ITO 29% Both 32% BPO ITO Both Out of the 28 outsourcing companies most of the companies are involved in Business process outsourcing.

8. c) NET PROFIT DISTRIBUTION BPO 16% ITO 43% Both 41% BPO ITO Both From this it can be inferred that of the 28 out sourcing venders, ITO Companies contributes the most (43%) followed by the companies which outsource both ITOs and BPOs. On comparing the above pie charts, it can be inferred that though BPO’s are more in numbers the net profit is mainly contributed by the ITO sector.

9. QUANTITATIVE DATA ANALYSIS a) FIVE NUMBER SUMMARY 1)Minimum30 2) Lower Quartile Q 1 854 3)Median2250 4) upper Quartile Q 3 7300 5)Maximum86700

10. b) REVENUE DISTRIBUTION REVENUE No. of companies Q1853.757 Q222507 Q373008 Q4867006

11. The above given table shows the quartiles of revenue.The pie chart shows the graphical representation of the number of companies coming under Q1,Q2,Q3 and Q4. Quartile1 has 7 companies coming within it and constitutes 25% total revenue. Quartile2 includes 7 companies within it and constitutes 25% of total revenue.Quartile3 includes8 companies and constitutes 29% of total revenue. Quartile4 includes 6 companies and constitutes 21%of the total revenue.

12. c) FREQUENCY DISTRIBUTION TABLE Bin Mid value Frequen cy RFPFCF 0250150.5453.5753.57 50075070.2525.0078.57 1000125030.1110.7189.29 1500175000.000.0089.29 2000225000.000.0089.29 2500275000.000.0089.29 3000325010.043.5792.86 3500375010.043.5796.43 4000425000.000.0096.43 4500475010.043.57100.00 500028100.00 From the frequency distribution table we can construct Histogram, Percentage Frequency Curve and Ogive Curve.

13. HISTOGRAM Histogram is snapshot of the frequency distribution. Here the x axis represents the class (net profit) and y axis represents the frequency.

14. PERCENTAGE FREQUENCY CURVE Here the Relative frequency is expressed in percentages.

15. OGIVE CURVE The Ogive Curve is a graphical representation of the cumulative frequency distribution using numbers or percentages. Here the net profit values are on x axis and cumulative frequency in percentages are on y axis. A line graph in the form of a curve is plotted connecting the cumulative frequency. The net profit is the highest when the cumulative frequency is 100. From the above Ogive curve it is observed that the frequency first increases, then remains constant and slowly increases again. From the Ogive curve, any value on the X axis can be found just by dropping a line.

16. d) CORRELATION AND REGRESSION Correlation is a study that focuses on the strength of association or relationship between variables. Correlation coefficient: It measures the degree to which two interval scaled variables are linearly associated. It is a pure number that lies in the interval -1 - +1. There could be zero correlation, positive correlation or negative correlation. Regression is a process of predicting the value of the response variable that depends on one or more number of independent variable.

17. CORRELATION BETWEEN REVENUE AND EMPLOYEES

18. Karl Pearson’s correlation measures quantitatively the extent to which two variables are correlated. For a set of n pairs of value of x and y, Pearson’s correlation coefficient is given by, r= Cov(x, y)/ (σx *σy) Here coefficient of correlation between Revenue and Employee is 0.65.From this it can be inferred that there is substantial correlation between Revenue and Employee. Intercept-3596.04 Slope0.31 Regression eqn y=0.31x-3596

19. CORRELATION BETWEEN REVENUE AND PROFIT

20. Coefficient of correlation between Net Profit and revenue is 0.82. Here it is clear that there is a high correlation between the revenue and net profit. That is as revenue increase the net profit also increases. Intercept 218.5 5 Slope 0.049 7 Regression equation y=218.55+.9497

21. SPEARMAN’S RANK CORRELATION COEFFICIENT This method is applied to measure the association between two variables when only ordinal or rank data are available. Mathematically, spearman’s rank correlation coefficient is defined (SRCC) as R= 1- (6εd^2/n (n^2-1)) = 0.88 R=0.87 shows that the net profit is strongly associated with revenue. The coefficient of correlation varies between 0.7 and 1, shows that there is high positive correlation.

22. e) PROBABILITY DISTRIBUTION Services/EmployeeBPOITOBOTHTotal 0-2500084315 25000-500002114 50000-750001135 75000-1000000101 100000-1250000022 125000-1500000101 Total118928 From these various probabilities can be calculated of which some of them are given below: Probability that a company being both (BPO&ITO) and having 550000 employees is 0.33 Probability that a given company is BPO 0.39 Probability that a given company is an ITO and has 20000 employees is 0.14

23. THANK YOU