Telecom Sector in India

Telecom Sector in India2013

TELECOM SECTOR IN INDIA

ContentsTable of Figures:21) Introduction:42)Objective:53)Sampling54) Probability64.1) Simple Probability64.2) Conditional Probability65)Descriptive Statistics85.1 Average Revenue per user for Aircel :85.2 Average Revenue per user for BPL :105.3 Average Revenue per user for Vodafone:125.4 Average Revenue per user for Airtel:145.5 Average Revenue per user for Idea:166)Hypothesis testing186.1) One Sample t-test186.2) One-Way Anova247)Regression29

1) Introduction:

1) Indias telecommunication is the second largest in the world. The total number of mobile subscribers in May12 was 92.9 crores. Telecommunication is one of the industries which have shown rapid growth post-liberalization. 2) India has opted for both GSM and CDMA technologies in the mobile sector. However, GSM is very popular in India. Out of the 92.9 crore subscribers, 81.4 crore use GSM network. Telecom Regulatory Authority of India (TRAI) provides guidelines, specifications and regulations for the telecom industry in India. Vodafone, Airtel, Idea, Aircel, Reliance, Tata Docomo are some of the leading service providers in India. 3) Average Revenue Per User (ARPU) is a term commonly used by telecommunication companies, defined as total revenue divided by number of subscribers. It represents the revenue contributed by a single customer per month. In India, ARPU has been declining in India over the past few years because of declining tariffs caused by stiff competition among service providers and increasing subscribers. But it still gives the service provider valuable information on the average revenue generator.4) Data Source:1. http://www.coai.com (Cellular Operator Association of Indias website)2. http://www.cmie.com/3. www.trai.gov.in

2) Objective:

To prepare the sample of Average Revenue per User (ARPU) for various GSM service providers. To determine the trend of ARPU for the sample and various service providers in the sample. To determine the conditional probability of ARPU for various service providers in a particular circle (UP East). To check whether the ARPU per month of telecom industry is equal to sample ARPU. To check whether the ARPU per month for telecom industry has changed over a period of 4 years. To develop a regression model between revenue per month and ARPU per month for the telecom industry for all quarters from 2008-09 to 2011-12.3) Sampling

Stratified sampling is one of the probabilistic sampling techniques used. A stratum is defined by some common characteristic, such as gender or year in school. Strata are homogeneous within and heterogeneous from outside. The homogeneity of items within each stratum provides greater precision in the estimates of underlying population paramerters.For, fiscal years 2008-09, 2009-10, 2010-11, 2011-12, we have gathered the ARPU for Aircel, Airtel, BPL, Idea and Vodafone for all the four quarters. As per Trai website there are 12 service providers in the GSM Sector. They are Bharti Airtel, Aircel, Reliance Communication, Tata Docomo, MTS (Siesta Shyam), Vodafone, Idea, Uninor, MTNL, BSNL, BPL Loop and Quadrant Televentures. The data set of these companies is strata i.e they are homogeneous within and heterogeneous outside, as there are many parameters which change from company to company such as marketing strategy, sales and promotion policy, market share, sector wise promotion. We have picked up randomly 5 companies from the population and formed our sample. The sample size, n = 5 and it includes aircel, airtel, Vodafone, BPL and Idea.

4) Probability4.1) Simple Probability

P(ARU = 0.05, accept Ho. So population values are normally distributed.Hypothesis testing,Ho: = H1: Perform one-sample t test

One-Sample Statistics

NMeanStd. DeviationStd. Error Mean

FY_20105158.689532.3273914.45725

Figure 24: Table for Sample Statistics.

One-Sample Test

Test Value = 158.69

TDfSig. (2-tailed)Mean Difference95% Confidence Interval of the Difference

LowerUpper

FY_2010.00041.000-.00050-40.140340.1393

Figure 25: Table for One Sample t Test

As p(1) > (0.05), so we do not reject the null hypothesis. So, for fiscal year 2009-10, there is no significant evidence to suggest that ARPU per month of telecom industry is not Rs.158.69.

ARPU (in Rs.) for Fiscal year 2010-11To check whether average ARPU of telecom industry for fiscal year 2010-11 is equal to sample mean ARPU.AircelBPL mobileIdeaVODAFONEAirtelTotal

80.72151.85129.14130.16153.75129.12

Figure 26: Comparative view of different statistics

Ho: Population follows normal distributionH1: Population does not follow normal distribution.Confidence level assumed is 95% (so = 0.05).After performing Kolmogorov-Smirnov test,

Tests of Normality

Kolmogorov-SmirnovaShapiro-Wilk

StatisticdfSig.StatisticdfSig.

FY_2011.3005.160.8425.170

Figure 27: Table for Normality

As significant value, p= 0.16 > = 0.05, accept Ho. So population values are normally distributedHypothesis testing,Ho: = H1:

Perform one-sample t test (confidence level = 95 %, = 0.05)

One-Sample Statistics

NMeanStd. DeviationStd. Error Mean

FY_20115129.122029.4424013.16704

Figure 28: Table for One Sample Statistics

One-Sample Test

Test Value = 129.12

TDfSig. (2-tailed)Mean Difference95% Confidence Interval of the Difference

LowerUpper

FY_2011.00041.000.00200-36.555636.5596

Figure 29: Table for One Sample t Test

As p(1) > (0.05), so we do not reject the null hypothesis. So, for fiscal year 2010-11, there is no significant evidence to suggest that ARPU per month of telecom industry is not Rs.129.12.

ARPU (in Rs.) for Fiscal year 2011-12AircelBPL mobileIdeaVODAFONEAirtelTotal

67.11152.97118.02122.76140.04120.18

Figure 30: Comparative view of ARPUs

Ho: Population follows normal distributionH1: Population does not follow normal distribution.Confidence level assumed is 95% (so = 0.05)After performing Kolmogorov-Smirnov test,

Tests of Normality

Kolmogorov-SmirnovaShapiro-Wilk

StatisticDfSig.StatisticdfSig.

FY_2012.2745.200*.9065.441

Figure 31: Table for Normality

As significant value, p( 0.2) > ( 0.05), accept Ho population values are normally distributed

Hypothesis testing,Ho: = H1: Perform one-sample t test (confidence level = 95 %, = 0.05)One-Sample Statistics

NMeanStd. DeviationStd. Error Mean

FY_20125120.176532.7778114.65868

Figure 32: Table for One Sample Statistics

One-Sample Test

Test Value = 120.17

TdfSig. (2-tailed)Mean Difference95% Confidence Interval of the Difference

LowerUpper

FY_2012.00041.000.00650-40.692540.7055

Figure 33: Table for One Sample t Test

As p = 1 > = 0.05, so we do not reject the null hypothesis. So, for fiscal year 2011-12, there is no significant evidence to suggest that ARPU per month of telecom industry is not Rs.120.17.6.2) One-Way Anova

We have established that ARPU per month for telecom industry for each year from 2008-09 to 2011-12 is equal to sample mean. Following is the table for sample ARPUs for each fiscal year. Using One-way anova we will check if population mean for all years from 2008-09 to 2011-12 are equal.

Operator2008-092009-102010-112011-12

Aircel148.68105.3780.7267.11

BPL248.8160.79151.85152.97

Idea214.59166.74129.14118.02

Vodafone232.66167.34130.16122.76

Airtel256.22193.23153.75140.04

Figure 34: ARPUs for different operators in different years

ARPU per month for different operators each fiscal year from 2008-09 to 2011-12

First the assumptions of one-way anova are to be checked.1. Populations for all years are normally distributed: Kolmogorov-Smirnov test is used to check whether populations are normally distributed. In Section 6.1, Kolmogorov-Smirnov test has been performed and it has been established that all populations are normally distributed.2. Homogeneity of variances: Variances of all populations need to be equal to perform one way Anova test.Ho: Variances are homogenousH1: Variances are not homogenous

Test of Homogeneity of Variances

ARPU

Levene Statisticdf1df2Sig.

.259316.854

Figure 35: Table for Homogeneity of Variances

As p(0.854) > (0.05), accept Ho. So variances are homogenous.4. Samples are randomly drawn and independent:From the Box Plot drawn for all years it can be seen that Median for all four samples are not equal. This proves the assumption is correct. As all three assumptions have been satisfied, One-way anova can be performed.

Figure 36: Box Plot for different Vendors.

Ho: Average ARPU per month for each fiscal year from 2008-09 to 2011-12 are equal. (All four population means are equal)H1: At least one average ARPU per month for a fiscal year is different from the rest.

ANOVA

ARPU

Sum of SquaresdfMean SquareFSig.

Between Groups30643.103310214.3688.441.001

Within Groups19361.585161210.099

Total50004.68919

Figure 37: Table for Anova

As p (0.001) < (0.05), reject Ho. There is sufficient evidence to suggest that average ARPU for at least one fiscal year from 2008-09 to 2011-12 is different from the others. Post Hoc Tukey test has been performed to compare the average ARPUs for a pair of fiscal years. 1. Ho: ARPU per month (2008-09) = ARPU per month (2009-10)H1: ARPU per month (2008-09) ARPU per month (2009-10) From Post Hoc Tukey table p = 0.057 > = 0.05, accept Ho. Average ARPU for 2008-09 and 2009-10 are equal.

2. Ho: ARPU per month (2008-09) = ARPU per month (2010-11)H1: ARPU per month (2008-09) ARPU per month (2010-11) From Post Hoc Tukey table p = 0.004 < = 0.05, reject Ho. Average ARPU for 2008-09 and 2009-10 are not equal. Also from the table it can be seen that average ARPU for 2008-09 > average ARPU for 2010-11

3. Ho: ARPU per month (2008-09) = ARPU per month (2011-12)H1: ARPU per month (2011-12) ARPU per month (2011-12) From Post Hoc Tukey table p = 0.002 < = 0.05, reject Ho. Average ARPU for 2008-09 and 2010-11 are not equal. Also from the table it can be seen that average ARPU for 2008-09 > average ARPU for 2011-12

4. Ho: ARPU per month (2009-10) = ARPU per month (2010-11)H1: ARPU per month (2009-10) ARPU per month (2010-11) From Post Hoc Tukey table p = 0.55 > = 0.05, accept Ho. Average ARPU for 2009-10 and 2010-11 are equal.

5. Ho: ARPU per month (2009-10) = ARPU per month (2011-12)H1: ARPU per month (2009-10) ARPU per month (2011-12) From Post Hoc Tukey table p = 0.332 > = 0.05, accept Ho. Average ARPU for 2009-10 and 2011-12 are equal

6. Ho: ARPU per month (2010-11) = ARPU per month (2011-12)H1: ARPU per month (2010-11) ARPU per month (2011-12) From Post Hoc Tukey table p = 0.977 > = 0.05, accept Ho. Average ARPU for 2010-11 and 2011-12 are equalWe can draw the following conclusion based on the above results. ARPU per month (2008-09) > ARPU per month (2009-10)ARPU per month (2008-09) > ARPU per month (2010-11)ARPU per month (2008-09) > ARPU per month (2011-12)ARPU per month (2009-10) = ARPU per month (2010-11)ARPU per month (2009-10) = ARPU per month (2011-12)ARPU per month (2010-11) = ARPU per month (2011-12)The ARPU per month (2008-09) is the highest for all given four years. It can also be said that ARPU has not changed significantly since 2009-10. Multiple Comparisons

Dependent Variable: ARPU

Tukey HSD

(I) year_labels(J) year_labelsMean Difference (I-J)Std. ErrorSig.95% Confidence Interval

Lower BoundUpper Bound

2008-092009-1061.4960022.00090.057-1.4490124.4410

2010-1191.06600*22.00090.00428.1210154.0110

2011-12100.01000*22.00090.00237.0650162.9550

2009-102008-09-61.4960022.00090.057-124.44101.4490

2010-1129.5700022.00090.550-33.375092.5150

2011-1238.5140022.00090.332-24.4310101.4590

2010-112008-09-91.06600*22.00090.004-154.0110-28.1210

2009-10-29.5700022.00090.550-92.515033.3750

2011-128.9440022.00090.977-54.001071.8890

2011-122008-09-100.01000*22.00090.002-162.9550-37.0650

2009-10-38.5140022.00090.332-101.459024.4310

2010-11-8.9440022.00090.977-71.889054.0010

Figure 38: Table for Tukeys Test.* The mean difference is significant at the 0.05 level.

7) Regression

To develop a regression model to study the relationship between revenue per month of the telecom industry and ARPU per month using the data for each quarter from 2008-09 to 2011-12.QuarterRevenue per month (Rs. Crores)ARPU (Rs.)

Q1 2008-09761.2213333244.858

Q2 2008-09783.1573333222.424

Q3 2008-09891.9253333214.994

Q4 2008-09921.9473333198.232

Q1 2009-10943.554181.596

Q2 2009-10928.196164.442

Q3 2009-10912.124148.424

Q4 2009-10959.4133333140.3

Q1 2010-111007.684137.026

Q2 2010-11988.216129.002

Q3 2010-111031.19127.442

Q4 2010-111052.098123.018

Q1 2011-121125.1123.82

Q2 2011-121113.75115.93

Q3 2011-121187.728120.378

Q4 2011-121217.726120.578

Figure 39: Revenue and ARPU per month details for each Quarter from 2008-09 to 2011-12Following steps are followed:1. The basic equation of single regression model is written as: (ARPU per month) = 0 + 1 * (Revenue) + e Here ARPU per month is the dependent variable and revenue is the independent variable.2. The SPSS output is shown below. First we have performed a basic model check. Ho: i = 0 H1: At least one i 0 From the Anova table p = 0.000 < = 0.05. So reject Ho. All i are significant.3. 75.2% variation in ARPU per month can be explained by variation in revenue. Refer R square column of Model summary table.4. Now we will check the significance of independent variable. Ho: 1 = 0 H1: 1 0 From Coefficients table p = 0.000 < = 0.05. So reject Ho. 1 0. So independent variable is significant.5. Now we rewrite the regression model using the SPSS output From coefficients table 0 = 435.992; 1 = -0.282 = 4385.992 -0.282X = expected value of ARPU (in Rs.) X = value of revenue (in Rs. crore)6. Now the residuals analysis is performed on the model to check the assumptions of regression analysis are valid or not. (i) Linearity: The relationship between variables is linear. Ho: = 0 H1: 0 P = 0.000 < = 0.05 (from correlations table). So reject Ho. The variables are linearly related. (ii) Independence of errors: This assumption requires that errors are independent of each other. As our data set is not time series based, this assumption need not be checked. (iii) Normality: This assumption requires that errors are normally distributed. Referring the normality plot we can conclude that errors are not normally distributed. So this assumption is violated. However we will continue to use the developed model. (iv) Equal variances: Homoscedasticity requires that variance of errors are constant for all values of x. Looking at the scatter plot between residuals and predicted value it can be seen that no fanning is observed. So the assumption of homoscedasticity is valid. Thus we can conclude that model developed in point 5 is valid and can be used to make Model Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate

1.867a.752.73421.77417

a. Predictors: (Constant), revenue

b. Dependent Variable: ARPUFigure 40: Model Summary

ANOVA

ModelSum of SquaresDfMean SquareFSig.

1Regression20104.569120104.56942.404.000b

Residual6637.60214474.114

Total26742.17115

a. Dependent Variable: ARPU

b. Predictors: (Constant), revenue

Figure 41: Table for Anova Coefficients

ModelUnstandardized CoefficientsStandardized CoefficientstSig.Collinearity Statistics

BStd. ErrorBetaToleranceVIF

1(Constant)435.99243.18410.096.000

revenue-.282.043-.867-6.512.0001.0001.000

a. Dependent Variable: ARPU

Figure 42: Table for Coefficients. Collinearity Diagnostics

ModelDimensionEigenvalueCondition IndexVariance Proportions

(Constant)revenue

111.9921.000.00.00

2.00815.8031.001.00

a. Dependent Variable: ARPU

Figure 43: Table for Collinearity DiagnosticsResiduals Statistics

MinimumMaximumMeanStd. DeviationN

Predicted Value92.5356221.2915157.029036.6101716

Residual-30.3058330.56720.0000021.0358516

Std. Predicted Value-1.7621.755.0001.00016

Std. Residual-1.3921.404.000.96616

a. Dependent Variable: ARPU

Figure 44: Table for Residual Statistics

Figure 45:Normal PP Plot

Figure 46: Graph For residual Value.

Correlations

RevenueARPU

revenuePearson Correlation1-.867**

Sig. (2-tailed).000

N1616

ARPUPearson Correlation-.867**1

Sig. (2-tailed).000

N1616

**. Correlation is significant at the 0.01 level (2-tailed).

Figure 47: Table for Correlation

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