final bqt report

of 78 FINAL REPORT OF BQT

Authors Detail:

This Report of BQT project is written by the Following persons at the end of the 5th semester…

Name: Raheel Tariq

ID: 08108048

Student of BBA horns in Gift University

Email ID: [email protected]

Name: Aisha Riaz

ID: 08108042



Name: Komal Afzal

ID: 08108034



mailto:[email protected]




Acknowledgement:

First of all we want to register our thanks to Prof. Sir. Abid Awan. Who opened our mind and shaped our future and have always been a friend, guide and philosopher to us. It was his inspiration and unbounded zeal that has created this project

Also thanks to Mr. Sarmad Hasan Territory sales Supervisor of Telenor, Mr. Tanweer Hussain Specialist customer care of Jazz and Mr. Usman Ahmed Regional Coordinator of Ufone who has given us enormous material support to our Project. It was quite pleasant for us that such nice people still exist in this world of sins. He gave us his precious time and briefed us personally during the visit.

We find an opportunity while writing this page to say thanks to our parents whom sleepless nights enabled us to accomplish this project. We always pray Allah for their good health. Their Shadow always remains on our head (Amen).

Last but not least, we want to register our thanks to our class fellows for being so cooperative, uncomplaining and no interrupting. It’s their matchless cooperation which enables us to study in the class peacefully.


Objective:

The purpose of this project is to help you gain experience with the collection of data. The presentation of the data, and the analysis of the data. We also needed to choose appropriate techniques for answering questions. We applied all the statistical tools in our project studied during the whole session. The main basic objective of doing this project was to understand the use of the statistical tools in the real world.


Table of Contents:

Sr No Contents Page no1 Authors Detail 12 Acknowledgement 23 Objective 34 List of Tables 55 List of figures 66 List of Abbreviations 77 History of Jazz 88 History of Ufone 99 History of Telenor 1010 Description of Statical Tools 11-1211 Description of Variables 1312 Descriptive Analysis 18-2913 Analysis of Variance 30-4314 Correlation Analysis with Hypothesis 44-5115 Regression Analysis with Hypothesis 52-6416 Comparison Among Companies 65-6617 Crux of Study 6718 Recommendation and Suggestions 6819 Conclusion 6920 Appendix 7021 Supporting Material 71


List of Tables:

Sr no Contents Page no1 Table For Raw Data of Jazz 142 Table For Raw Data of Ufone 153 Table For Raw Data of Telenor 164 Table For Mean of Jazz 185 Table For Mean of Ufone 196 Table For Mean of Telenor 207 Table For Standard Deviation of Jazz 238 Table For Standard Deviation of Ufone 249 Table For Standard Deviation of Telenor 2510 Table of ANOVA For Advertisement 3311 Table of ANOVA For Customers 3712 Table of ANOVA For Sims Sold 4113 Table of Correlation of Jazz 4414 Table of Correlation of Ufone 4515 Table of Correlation of Telenor 4616 Table of Multiple Regression of Ufone 5217 Table for Multiple Regression of ANOVA 5618 Table of Multiple Regression of Jazz 5719 Table for Multiple Regression of ANOVA 6020 Table of Multiple Regression of Telenor 6121 Table for Multiple Regression of ANOVA 64


List of Figures:

No Figures


List of Abbreviations:

X1 = Advertisement (Independent Variable)X2 = no of Customers (Independent Variable)y = Sims Sold in units (Dependent Variable)

= is the arithmetic mean of the values.

S.D = Standard deviation

M.D = Mean Deviation

C.V = coefficient of variance

Sk = Skewness

µ

1

= mean of company one

µ

2

= mean of second company

µ

3 =

mean of third company

SST = Total Sum of square

SSR = Regression Sum of square

SSE = Error Sum of square

k= no of categories

Df = Degree of freedom

n= is the no of observation in the sample.


= indicates the absolute value.

a = the y- intercept of the estimated plane which is the best point estimate of α.

b 1 = The slope of the estimated plane with x1 which is the best point estimate of β1 and it is changed by the value of x1.

b 2 = The slope of the estimated plane with x2 which is the best point estimate of β2 and it is changed by the value of x2.

HISTORY OF JAZZ

Pakistan Mobile Communications Limited, better known as Mobil ink GSM, is a telecommunication service provider in Pakistan. The company is Pakistan's leading cellular operator with a subscriber base of 31.5m and market share of 31% in October 2010.

Mobil ink’s Head office is located at Mobil ink House, 1-A Kohistan Road, F-8 Markaz Islamabad. Mobil ink’s corporate postpaid package is sold under the brand name "Indigo" and prepaid by the name of "Jazz".

Mobil ink started operations in 1994 as the first GSM cellular Mobile service in Pakistan by MOTOROLA later it was sold to Orascom, an Egypt-based multi-national company.

In addition to cellular service, the Orascom group is diversifying its service portfolio by setting up new businesses and also expanding through acquisitions. Recently, they started offering DSL broadband through a wholly owned subsidiary, Link.Net.

In addition to Mobil ink, the Orascom group also owns TWA (Trans World Associates) which operates an undersea fiber optic cable from Karachi to Fujairah, UAE.

Till June'10 the company had issued two listed bonds to the tune of Rs. 3.2 billion and Rs. 6 billion

http://en.wikipedia.org/wiki/Egypt

http://en.wikipedia.org/wiki/Orascom

http://en.wikipedia.org/wiki/MOTOROLA

http://en.wikipedia.org/wiki/Pakistan

http://en.wikipedia.org/wiki/Mobile_phone

http://en.wikipedia.org/wiki/Cellular_network

http://en.wikipedia.org/wiki/GSM


History of Ufone:

In 1990, Pakistan introduced its first mobile phone service called “Paktel”. After eleven years in January 2001, a new cellular company with GSM technology came into the market and they named it Ufone. The government of Pakistan granted them the license of Pak Telecom mobile limited to operate GSM 9000 all Around Pakistan. Ufone succeeded to establish itself in the market by providing quality service at low rates. After it’s opening, initially they started their service in major cities like Rawalpindi, Islamabad, Karachi and Lahore and on major highways. Later the service expanded to other major cities like Peshawar, Quetta and different towns. Ufone invested around $ 65 million to setup its modern technical infrastructure to provide high quality crystal clear voice and fast data transmission. Ufone targeted the middle class, by introducing low rates and different packages with Urdu names like Jazba etc to attracted lower and middle class people. Ufone has expanded its customer support in a very organized manner with a planned network of dealers, outlets for people convenient. Ufone is committed to care for its customers even after they have acquired a new connection. Ufone not only focused on the technical capabilities, but to provide subscribers a convenient to get connections and services. Ufone recently started GPRS through which users can connect themselves to the global village via Internet, also introduced multimedia messaging services and worldwide SMS at flat rates. Not only SMS but also provides Ufone Info service through which user can get latest information about news, sports, religion, horoscope, stock exchange etc even live sports updates. The tariff packages have been designed keeping in mind the requirements of every segment of the society may that be a housewife, a taxi driver, a trader or a student. Ufone started its operation from Islamabad on 29th January 2001. Cellular industry performance in Pakistan before the launch of Ufone was quite dismal, with one of the lowest population penetration rates in the Whole region. Ufone’s strategy from the day one was to change this scenario and ensure that mobile phones are turned into an everyday business and personal communication tools for all.


History of Telenor:

Telenor Pakistan is 100% owned by the Telenor Group, an international provider of high quality voice, data, content and communication services in 14 markets across Europe and Asia. Telenor Group is among the largest mobile operators in the world with over 195 million mobile subscriptions (Q3 2010) and a workforce of approximately 34,000.

Telenor Pakistan is the country's single largest European investor, with investments in excess of US$2 billion. It acquired a GSM license in 2004 and began commercial operations on March 15, 2005.

At the end of October 2010 it had a reported subscriber base of 24.12 million, and a market share of 24% making it the country's second largest mobile operator.


.Brief description of all Statical Tools:

We apply Different Statical tools on our Project e.g.1: Mean 2: Median 3: Standard deviation 4: Mean deviation 5: coefficient of variance6: skewness7: Regression Analysis8: ANOVA

Mean shows the average means it shows the average sale per month, the mean tells us the company’s average per month and it helps in comparing one company to the other.While median also tells us the average but the difference between the mean and the median is Mean is appropriate when data is symmetrical, because some month’s has less sale or some has greater so for that condition mean is not the appropriate tool then we use median of data having outlay in it because an outlay leads the data at extreme. Thus to overcome this problem we use median to calculate average.It is also the type of average but we calculate it when the data is non- symmetrical.Standard Deviation tells that how much the data is scattered about the mean, the smaller S.D located close to the mean conversely a large S.D reveals that the observations are widely scattered about the mean.The M.D shows how much the data deviates from its mean.When we find that what percent deviation is from mean? Then we use the rule to measure this coefficient of variation that tells how much percent the data deviates from the mean.Skewness tells us that whether the data is Right Skewed or the left skewed. Skewness > 0 - Right skewed distribution - most values are concentrated on left of the

mean, with extreme values to the right. Skewness < 0 - Left skewed distribution - most values are concentrated on the right of

the mean, with extreme values to the left. Skewness = 0 - mean = median, the distribution is symmetrical around the mean.


Anova is a procedure use to test the null hypothesis that the mean of three or more populations are all equal. The procedure we are discussing is called the analysis of variance because the test is based on the analysis of variance in the data obtained from different samples, the application of the one way ANOVA requires the following assumptions, hold true

ASSUMPTIONS OF ONE WAY ANOVA

The population from which the samples are drawn are (approximately) normally distributed

The population from which the samples are drawn have the same variance or standard deviation

The samples drawn from different populations are random and independent.

The ANOVA test is applied by calculating two estimates of the variance of the population distribution the variance between samples and the variance within samples. The variance between samples is also called mean square between sample or MSB. The variance within samples is also called the mean square within sample or MSW.

The variance between samples gives an estimation of variance based on the variation among the means of the samples taken from the different population.

If the means of all population under consideration are equal the means of the respective sample will still be different but the variation among them is expected to be small and consequently the value of MSB is expected to be small. However the mean of the population under consideration are not equal then the value of the MSB is expected to be large. The one way ANOVA test is always right tail with the rejection region in the right of the F distribution curve.

And the Last one is the Multiple Regression and the Correlation that tells us about the Relationship between the dependent and the independent variable and the value of correlation tells us whether the relationship between the dependent and the independent variable is Strong positive or negative and how much the model is best fitted.


Brief description of Variables:

We have selected three Companies Ufone, Jazz and Telenor and also Variables that are

1. Advertisement2. No of Customers3. No of Sims Sold

Independent:

Advertisement:

Advertisement is our independent variable and it effects on our sale. No of customers:

No of customers is an independent variable and it also effects on our sale.

Dependent:

No of Sims Sold:No of Sims sold is our dependent variable and it depends upon Advertisement and no of customers.

We collected the thirty months data of these variables from the sales service center of

Ufone, Jazz and Telenor, So that we can apply all the Statical tools on the data through

these three variables.


As the Advertisement and no of customers are the independent variable and Sims Sold

are the dependent variable so to check the Relationship between these variables we apply

the Regression and the correlation Analysis on the thirty months data to check whether

the Sims Sold and the Advertisement, no of customers have Direct Relation or the in

direct Relation means for example Weather the sale will increase with the increase in the

Advertisement or not?Raw Data of jazz

Sr No

Year Month Advertisement No. Of Customers No. of Sims Sold

1 2008 June3751000 15339 85551

2 2008 July 3432000 14000 813303 2008 Aug 3000000 14530 785554 2008 Sept 2721000 13880 653305 2008 Oct 3545000 16580 771006 2008 Nov 3233000 14775 733807 2008 Dec 2843000 12335 613318 2009 Jan 2900000 13557 645239 2009 Feb 3100000 14330 6738210 2009 Mar 3300000 15000 6967011 2009 April 3200000 15600 7000012 2009 May 3500000 15890 7188513 2009 June 3700000 16338 7232014 2009 July 3951000 17870 7412315 2009 Aug 3104000 13280 7030016 2009 Sept 3211000 14000 7155017 2009 Oct 3623000 15330 73115


18 2009 Nov 3737000 16110 7400019 2009 Dec 3508000 15870 7300020 2010 Jan 2915000 13320 7110821 2010 Feb 2826000 12890 6980022 2010 Mar 2918000 13115 7000023 2010 April 3000000 14310 6890024 2010 May 3371000 14990 8600025 2010 June 3547000 15000 8755026 2010 July 3725000 15670 9000027 2010 Aug 3111000 13800 8050028 2010 Sept 2715000 12000 7589029 2010 Oct 2803000 13200 7870030 2010 Nov 3408000 14500 85370

Raw Data of UfoneSr No Year Month Advertisement No. Of Customers No. of Sims Sold

1 2008 June 3832000 6750 440002 2008 July 3730000 6160 42550

3 2008 Aug 3500000 7120 41800

4 2008 Sept 3808000 5110 39310

5 2008 Oct 3929000 4220 43880

6 2008 Nov 3708000 4000 40000

7 2008 Dec 3800000 5300 43800

8 2009 Jan 3513000 5050 40000

9 2009 Feb 3617000 5711 42900

10 2009 Mar 3615000 6150 41670

11 2009 April 3822000 7033 42100

12 2009 May 4800000 7881 49750

13 2009 June 4908000 8100 52810

14 2009 July 4970000 7750 40760

15 2009 Aug 3800000 6952 43752

16 2009 Sept 3600000 6509 40000

17 2009 Oct 3700000 6800 40710

18 2009 Nov 4000000 6000 46000

19 2009 Dec 4325000 7220 48751


20 2010 Jan 4400000 6315 35000

21 2010 Feb 4500000 6714 37000

22 2010 Mar 4313000 5833 39980

23 2010 April 4633000 6124 45609

24 2010 May 4750000 6800 48000

25 2010 June 4831000 7810 50000

26 2010 July 5000000 7650 42660

27 2010 Aug 4625000 4553 40000

28 2010 Sept 4700000 5720 41678

29 2010 Oct 4653000 5209 44749

30 2010 Nov 4900000 3880 50909

Raw Data of TelenorSr No

Year Month Advertisement No. Of Customers No. of Sims Sold

1 2008 June 2700000 8980 920002 2008 July 2800000 9000 935003 2008 Aug 1500000 8500 800004 2008 Sept 2000000 8750 810005 2008 Oct 2500000 7000 830006 2008 Nov 2508850 6500 700007 2008 Dec 2500000 6300 670008 2009 Jan 2900000 5500 650009 2009 Feb 2100000 5800 4500010 2009 Mar 2000000 5300 4185011 2009 April 2200000 5600 4200012 2009 May 2800000 5750 4375013 2009 June 2600000 5980 4000014 2009 July 2709800 6000 4100015 2009 Aug 2000000 5600 4100016 2009 Sept 2100000 6000 4389017 2009 Oct 2800000 6400 4300018 2009 Nov 2600000 6100 4100019 2009 Dec 2200000 6050 4077020 2010 Jan 2400000 6090 4165021 2010 Feb 1900000 4000 3600022 2010 Mar 2100000 4200 3800023 2010 April 1800000 4500 3300024 2010 May 2000000 4600 34000


25 2010 June 2400000 4800 3500026 2010 July 2000000 4500 3500027 2010 Aug 2400000 5000 3800028 2010 Sept 2400000 5100 4000029 2010 Oct 2700000 5400 5400030 2010 Nov 2500000 5300 50000

Explanation: of Raw Data:We collected this data from the head offices of Jazz, Ufone, Telenor in the Gujranwala Region of last 30 months of three variables that we have selected for our project, In these variables Advertisement and no of customers are independent while the no of Sims sold per month is dependent variable, then we divide the data of Advertisement and the no of Sims sold by 1000 for making our calculation easy then we apply the all the business quantitative techniques on this data.


Mean of Jazz

Sr. No

Mean of Advertisement

Mean of no of customers

Mean of Sims Sold


123456789101112131415161718192021222324252627282930

375134323000272135453233284329003100330032003500370039513104321136233737350829152826291830003371354737273111271528033408

15.314.014.513.916.614.812.313.614.315.015.615.916.317.913.314.015.316.115.913.312.913.114.315.015.015.713.812.013.214.5

85.681.378.665.377.173.461.364.567.469.770.071.972.374.170.371.673.174.073.071.169.870.068.986.087.690.080.575.978.785.4

97700 437.4 2238.26

Mean of Ufone

Sr. No



Mean of Sims Sold


123456789101112131415161718192021222324252627282930

383237303500380839293708380035133617361538224800490849703800360037004000432544004500431346334750483150004625470046534900

675061607120511042204000530050505711615070337881810077506952650968006000722063156714583361246800781076504553572052093880

4442.5541.839.3143.88

4043.840

42.941.6742.149.7552.8140.7643.75

4040.71

4648.75

3537

39.9845.64850

42.6640

41.6744.7550.9

126282 186424 1300.1

Mean of Telenor

Sr. No



Mean of Sims Sold


123456789101112131415161718192021222324252627282930

270028001500200025002509250029002100200022002800260027092000210028002600220024001900210018002000240020002400240027002500

898090008500875070006500630055005800530056005750598060005600600064006100605060904000420045004600480045005000510054005300

9293.580818370676545

41.8542

43.75404141

43.894341

40.7741.65

36383334353538405450

70,118 178,600 1530

Mean


Formula

Variables Advertisement No of Customers Sims Sold

Jazz

= 3257 = 14.58 = 74.60

Ufone

= 4210 = 6.214 = 43

Telenor = 2337.27 = 5.253 X = 51

Interpretation:

Mean shows the averagely advertisement of Jazz in Gujranwala region.Mean of advertisement is 3257000 in actual because we divide the data by 1000.so the Mean of advertisement of jazz is 3257 that shows the average advertisement per month in Gujranwala regionAnd the mean of no of customers is 14.58 that show averagely 14580 persons visit the sales service center of jazz in a month.Mean of Sims sold is 74.60 this also divided by 1000 so the mean is 74600 that shows jazz sales averagely 74600 Sims per month.

Comparison:


Averagely advertisement of these three companies jazz, ufone and telenor are 3257000, 4210000 and 2337000 respectively. Ufone spend more money on advertisement than the jazz and telenor.The strength of the no of customers of jazz that is 15000 is greater than Ufone and telenor.Averagely no of Sims sold of jazz, ufone and telenor are 75000, 43000 and 51000 respectively. sales of jazz is comparatively high because most of the people like business mans and households use the jazz network and also because of this reason that jazz is first telecommunication company in Pakistan. Secondly the telenor average sales are 51000 and greater than the ufone’s sales because they sale directly from their sales service center in Gujranwala.

Median Formula Median = (n+1) th value


2

Variables Advertisement No of Customers Sims SoldJazz 3222 14.5 73.05

Ufone 4156.5 6.2375 42.605

Telenor 2400 5.775 41.925

Interpretation:

Mean is appropriate when data is symmetrical, because some month’s has less no of unit’s sold or some has greater so for that condition mean is not the appropriate tool then we use median of data having outlay in it because an outlay leads the data at extreme. Thus to overcome this problem we use median to calculate average.It is also the type of average but we calculate it when the data is non- symmetrical.

Comparison:

Averagely advertisement of these three companies jazz, Ufone and telenor are 3222000, 4156500 and 2400000 respectively. Ufone spend more money on advertisement than the jazz and telenor.The strength of the no of customers of jazz that is 15000 is greater than Ufone and telenor.Averagely no of Sims sold of jazz, Ufone and telenor are 73000, 43000 and 41000 respectively. sales of jazz is comparatively high because most of the people like business mans and households use the jazz network and also because of this reason that jazz is first telecommunication company in Pakistan. There was large out layer in the data of telenor and when we apply median on the data than the average Sims sold of telenor is 4190 which is comparatively less than the sales of Ufone.

Standard Deviation of Jazz:


Standard Deviation of Advertisement

Standard Deviation of No of Customers

Standard Deviation of Sims Sold

X X² X X² X X² 3751

34323000272135453233284329003100330032003500370039513104321136233737350829152826291830003371354737273111271528033408

14070001.011778624.09000000.07403841.012567025.010452289.08082649.08410000.09610000.010890000.010240000.012250000.013690000.015610401.09634816.010310521.013126129.013965169.012306064.08497225.07986276.08514724.09000000.011363641.012581209.013890529.09678321.07371225.07856809.0

11614464.0

15.314.014.513.916.614.812.313.614.315.015.615.916.317.913.314.015.316.115.913.312.913.114.315.015.015.713.812.013.214.5

235.0196.0211.1192.7274.9218.3152.2183.8205.3225.0243.4252.5266.9319.3176.4196.0235.0259.5251.9177.4166.2172.0204.8224.7225.0245.5190.4144.0174.2210.3

85.681.378.665.377.173.461.364.567.469.770.071.972.374.170.371.673.174.073.071.169.870.068.986.087.690.080.575.978.785.4

7319.06614.66170.94268.05944.45384.63761.54163.24540.34853.94900.05167.55230.25494.24942.15119.45345.85476.05329.05056.34872.04900.04747.27396.07665.08100.06480.35759.36193.77288.0

97700 321751952 437.4 6430 2238.26 168482.4

Standard Deviation of Ufone:

Standard Deviation of Standard Deviation of No of Standard Deviation of


Advertisement Customers Sims SoldX X² X X² X X²

383237303500380839293708380035133617361538224800490849703800360037004000432544004500431346334750483150004625470046534900

146842241391290012250000145008641543704113749264144400001234116913082689130682251460768423040000240884642470090014440000129600001369000016000000187056251936000020250000186019692146468922562500233385612500000021390625220900002165040924010000

675061607120511042204000530050505711615070337881810077506952650968006000722063156714583361246800781076504553572052093880

455625003794560050694400261121001780840016000000280900002550250032615521378225004946308962110161656100006006250048330304423670814624000036000000521284003987922545077796340238893750337646240000609961005852250020729809327184002713368115054400

4442.5541.839.3143.88

4043.840

42.941.6742.149.7552.8140.7643.752

4040.71

4648.751

3537

39.9845.609

4850

42.6640

41.67844.74950.909

19361810.5031747.24

1545.2761925.454

16001918.44

16001840.41

1736.3891772.41

2475.0632788.8961661.3781914.238

16001657.304

21162376.66

12251369

1598.42080.181

23042500

1819.8761600

1737.0562002.4732591.726

126282 539417802 186424 1198344232 1300.128 56849.37

Standard Deviation of Telenor:


Standard Deviation of Advertisement

Standard Deviation of No of Customers

Standard Deviation of Sims Sold

X X² X X² X X²

270028001500200025002509250029002100200022002800260027092000210028002600220024001900210018002000240020002400240027002500

729000078400002250000400000062500006295081625000084100004410000400000048400007840000676000073386814000000441000078400006760000484000057600003610000441000032400004000000576000040000005760000576000072900006250000

898090008500875070006500630055005800530056005750598060005600600064006100605060904000420045004600480045005000510054005300

806404008100000072250000765625004900000042250000396900003025000033640000280900003136000033062500357604003600000031360000360000004096000037210000366025003708810016000000176400002025000021160000230400002025000025000000260100002916000028090000

9293.580818370676545

41.8542

43.75404141

43.894341

40.7741.65

36383334353538405450

846487426400656168894900448942252025175117641914160016811681192618491681166217351296144410891156122512251444160029162500

70118 167463762 178600 1115376400 1529.41 87835

Standard deviation


Formula

Variables Advertisement No of Customers

Sims Sold

Jazz 345 1.32 7.04

Ufone 511.41 1.15 4.5Telenor 345 1.31 19

Interpretation:

Standard Deviation tells that how much the data is scattered about the mean, the smaller S.D located close to the mean conversely a large S.D reveals that the observations are widely scattered about the mean.S.D of Advertisement of Ufone is 511000 and its mean is 4210000 that shows they can advertise 511000 more than its mean or less.We divide our data by 1000 that’s why in actual scenario company can advertise 345000 more or less from its mean that are in actual is 42, 10,000.S.D of no of customers is 1150 and the mean is 6214 that shows 1150 customers more or less can visit the sales service center from the mean.And the S.D of Sims sold are 4.5 and mean is 43 it was also divided by 1000 so company can sale 4500 Sims more or less from the mean that is in actual 43000.

Comparison:

Advertisements of Ufone is more deviated from its mean than jazz and telenor because Ufone focus and spend more on advertisement and no of customer of ufone are less deviated from its mean and sales of telenor are more deviated from its mean value.

Coefficient of Variance


Formula

Variables Advertisement No of Customers Sims SoldJazz 11% 9% 9%Ufone 12% 18% 10%Telenor 15% 22% 37%

Interpretation:

When we want to find that how much deviation is from mean in percentage, then we use coefficient of variation to measure this.

E.g. the C.V of Advertisement of jazz is 11% that tells us 11% deviation is from mean because mean does not give us the accurate value so we use it. And c.v of no of customers and Sims sold are 9% and 9% respectively that tells 9%data of both deviates from their means.

Comparison:

Advertisement of telenor is more deviated in percentage from its mean than jazz and Ufone and no of customer of jazz are less deviated from its mean and sales of telenor are more deviated from its mean value. this is because of the difference between the means of these three companies.


Skewness Formula

Variables Advertisement No of Customers Sims Sold

Jazz 0.30 0.18 0.66Ufone 0.32 -0.06 0.27

Telenor -0.55 0.41 1.42

Interpretation:

The Skewness of advertisement, no of customers and no of Sims sold of jazz 0.30, 0.18 and 0.66 that is greater than zero and it is Right skewed - most values are concentrated on left of the mean, with extreme values to the right.

Comparison:

Skewness values of Advertisement of jazz and Ufone shows that both are right skewed and advertisement of telenor is left skewed. no of customers of jazz and telenor is right skewed and no of customers of Ufone is left skewed. Sales of these three companies area right skewed.


Analysis of Variance:

Scenario:As the Analysis of the variance procedure is used to test the null hypothesis that the mean of the three or more population are the same against the alternative hypothesis that not all population mean are same. So here we want to test that the mean of the advertisement of the three companies Telenor jazz and Ufone are same or not?

ANOVA for advertisement

Step 1:- State the null and the alternative hypothesis

H

O

:

µ

1

= µ

2

= µ

3

H

1:

At least mean of one of the company’s Advertisement is not same.

Step 2:- Select the distribution to use

Because we are comparing the means for three normally distributed populations; we use

the F distribution to make this test.

Step 3:- Determine the rejection and the non rejection region

If f

cal >

f

tabulated

Then we reject H

O

that means we are in the favor of H

1

that is At least the Sims sold of

one of the company is not same.


V

1

(K-1) = 3-1 = 2

V

2

(N-k) = 90-3 = 87

f

tabulated

= v

1

, v

2

, α

= 2, 87, .05

F

tabulated

=

3.101296

Calculating the value of the test statistics:

The value of the test statistics F for a test of hypothesis using ANOVA is given by ratio of two variances’ the variance between sample and the variance within sample

Test statistics f for a one way ANNOVA:


The value of test statistics F for ANNOVA test is calculated as fallows

F=variance between sample/variance within sample =MSB/MSW

Source of Variance

df SS MS F(calculated)

Between Sample k-1 SSB MSB=SSB/k-1f=MSB/MSWWithin sample n-k SSW MSW=SSW/n-k

Total n-1 SST MST=SST/n-1

In ANNOVA terminology the three method use to find arithmetic means are called treatments the table contains the data on the no of customers of three companies.

Analysis of the variance procedure is used to test the null hypothesis that the mean of three or more populations are the same against the alternative hypothesis that not all populations mean are the analysis of variance can be used to compare two populations mean. In our project case we want to know that the means of the three companies are the same or not. First we want to check the mean of the no of customers of three companies, Telenor, Ufone, Jazz, To test these companies no of customers mean we test the null hypothesis That is H0: µ1 =µ2= µ3(all three population mean are equal)Against the alternative hypothesis H1: At least one population mean are not equalFor this test we are using ANOVA, short for the analysis of variance provides such a procedure. It is used to compare three or more population in a single test.X=no of customers of company K=no of different samples n1= size of the sample one n= number of values in all the samples =n1+n2+n3+……….∑x = the sum of the values in all samples = T1+T2+T3………∑x2 = the sum of the squares of the value of all the samplesTo calculate MSB and MSW we first compute the between-samples sum of squares denoted by SSB. And within sample sum of squares, denoted by SSW. The sum of SSB and SSW is called the total sum of squares and is denoted by SST, that is SST = SSB + SSW.

T1 is obtained by adding the values of the advertisement of the one sample, T2 is obtained by adding the values of the advertisement of the second sample, and we got T3 by adding the values of the advertisement of the third sample.


As there are 30 observations in each sample so, n1=n2=n3

Between- and Within-samples sum of squares. The between-samples sum of squares denoted by SSB, is calculated as

Source of Variance

df SS MS F(calculated)

Between Sample 2 52576987.6 26,288,493.8152Within sample 87 15000134.7 172,415.3414

Total 89 67577122.1 759,293.5067

Now, f

tabulated

is 3.1011296 and f

cal is

152 so we reject Ho, we are in the favor of H

1

that

means the mean of advertisement of at least one of the company is not same. In other

words, at least one of the mean is different from the other two means then to find in

which company the mean are different we apply the Fisher LSD method.


Fisher LSD


So here we use the Fisher LSD method to find that which mean is different from others.

If

> LSD (Then we reject Ho otherwise we don’t reject)

H

0

:µ

1

=µ

2

H

1

: µ

1

≠µ

2

H

0

:µ

2

=µ

3

H

1

: µ

2

≠µ

3

H

0

:µ

1

=µ

3

H

1

:µ

1

≠µ

3

If

>LSD we will reject H0.

= 1.96 (106.67) = 209.07

952.8>209.07So, We Reject Ho, we are in the favor of H1.

1872.1>209.07


So, We Reject Ho, we are in the favor of H1.

919.3>157.60So, We Reject Ho, we are in the favor of H1.

Conclusion: It means we are in the favor of H1, that means mean of Advertisement of all the three companies are different so we reject Ho.


Scenario:As the Analysis of the variance procedure is used to test the null hypothesis that the mean of the three or more population are the same against the alternative hypothesis that not all population mean are same. So here we want to test that the mean of the no of customers of the three companies Telenor jazz and Ufone are same or not?

ANOVA for the no of customers

H

O

:

µ

1

= µ

2

= µ

3

H

1:

At least mean of one of the company’s no of customers is not same.

If f

cal >

f

tabulated

Then we reject H

O


1

that is, at least the no of

customers of one of the company is not same.

f

t ab=

(v1

,

v2, α)

=2, 87, 0.05

= 3.101296

If f calculated > 3.101296 we will reject H

O.


Source of variation df SS MS fBetween sample 2 1444853152

722426576

43.5268Within sample 87 144396252.4

16597270.39Total89

15892494

178,567.348


O,

we are in the

favor of H

1

means at least mean of one of the company’s no of customers mean is

different from the other two means then to find in which company the mean are different

we apply the Fisher LSD method.


Fisher LSD

If



1.96 110537.8

1.96(33.4)

651.64

=8366.16667 and LSD=651.64

We will reject H0 because >LSD

=260.8

We will not reject H0 because <LSD

=8626.966



Conclusion: It means we are in the favor of H1 that at least one companies’ mean of no of customers is not same means we reject HO all the three means of no of customers are different.

Scenario:As the Analysis of the variance procedure is used to test the null hypothesis that the mean of the three or more population are the same against the alternative hypothesis that not all population mean are same. So here we want to test that the mean of the no of Sims sold of the three companies Telenor jazz and Ufone are same or not?ANOVA for Sims sold

H

O

:

µ

1

= µ

2

= µ

3


H

1:

At least mean of one of the company’s no of customers is not same.

If f

cal >

f

tabulated

Then we reject H

O


1

that is, at least the no of

customers of one of the company is not same.

f

t ab=

(v

1,

v

2

, α)

=2, 87, 0.05

= 3.101296


O.


Calculated Value:Source of variation df SS MS f

Between sample 2 15946.0 797358.495Within sample 87 11858.5 136.30

Total 89 27804.5 312.41

So, fcal =58.495> f tabulated=3.101296 therefore we reject H

o

we are in the favor of H

1

that is At least average of one of the company of Sims sold is different from the other two

means then to find in which company the mean are different we apply the Fisher LSD

method.


Fisher LSD

If

> LSD

Then we reject Ho otherwise we don’t reject.

H0:µ

1

=µ2 H1: µ

1

≠µ

2

H0:µ

2

=µ

3

H1: µ

2

≠µ

3

H0:µ

1

=µ

3

H1:µ

1

≠µ

3

If


= 5.8604


= and LSD=5.8604


=-7.64

We will not reject H0 because <LSD

=23.59666


Conclusion : It means we are in the favor of H1 and we reject HO that means all the three means of no of Sims sold are different.


.

COORELATION TABLE of Jazz:

X1 X2 Y Ŷ=a+b1x1+b2x2 e=Y-Ŷ e² Y-Ý (Y-Ý)²3832 6.75 44 42.47675 1.523247 2.320282 1 13730 6.16 42.55 41.8447 0.705298 0.497445 -0.45 0.20253500 7.12 41.8 41.648 0.151996 0.023103 -1.2 1.443808 5.11 39.31 41.52284 -2.21284 4.896654 -3.69 13.61613929 4.22 43.88 41.41984 2.460163 6.052402 0.88 0.7744

4040.61806-0.618060.381998-393708

38004 5.3 43.8 41.6 2.200002 4.840008 0.8 0.643513 5.05 40 40.57793 -0.57793 0.333998 -3 93617 5.711 42.9 41.25425 1.645753 2.708503 -0.1 0.013615 6.15 41.67 41.48353 0.186471 0.034772 -1.33 1.76893822 7.033 42.1 42.59761 -0.49761 0.247613 -0.9 0.814800 7.881 49.75 46.07839 3.671613 13.48074 6.75 45.56254908 8.1 52.81 46.53001 6.279994 39.43833 9.81 96.23614970 7.75 40.76 46.5341 -5.7741 33.34025 -2.24 5.01763800 6.952 43.752 42.48609 1.265907 1.602521 0.752 0.5655043600 6.509 40 41.62968 -1.62968 2.65585 -3 93700 6.8 40.71 42.09516 -1.38516 1.918678 -2.29 5.24414000 6 46 42.59426 3.405738 11.59905 3 94325 7.22 48.751 44.25419 4.496808 20.22128 5.751 33.0744400 6.315 35 44.00082 -9.00082 81.01477 -8 644500 6.714 37 44.52424 -7.52424 56.61412 -6 364313 5.833 39.98 43.47311 -3.49311 12.20181 -3.02 9.12044633 6.124 45.609 44.61927 0.989725 0.979556 2.609 6.8068814750 6.8 48 45.34386 2.656136 7.055061 5 254831 7.81 50 46.13622 3.863782 14.92881 7 495000 7.65 42.66 46.57328 -3.91328 15.31379 -0.34 0.11564625 4.553 40 43.75187 -3.75187 14.07656 -3 94700 5.72 41.678 44.60988 -2.93188 8.595899 -1.322 1.7476844653 5.209 44.749 44.19037 0.55863 0.312068 1.749 3.059001


4900 3.88 50.909 44.24174 6.667257 44.45232 7.909 62.55228126282 186.424 1300.128 1300.71 -0.58205 402.1382 10.128 508.3636

COORELATION TABLE OF Ufone:

X1 X2 Y Y^ e(Y-Y^) e2 Y-Y~ (Y-Y~)23751 15.339 85.551 82.70102 2.849983 8.122401 10.951 119.92443432 14 81.33 80.43876 0.89124 0.794309 6.73 45.29293000 14.53 78.555 69.17517 9.379829 87.98119 3.955 15.642032721 13.88 65.33 65.3731 -0.0431 0.001857 -9.27 85.93293545 16.58 77.1 73.87171 3.228294 10.42188 2.5 6.253233 14.775 73.38 73.39258 -0.01258 0.000158 -1.22 1.48842843 12.335 61.331 73.43667 -12.1057 146.5474 -13.269 176.06642900 13.557 64.523 70.40199 -5.87899 34.56252 -10.077 101.54593100 14.33 67.382 72.05303 -4.67103 21.81853 -7.218 52.099523300 15 69.67 74.0645 -4.3945 19.31163 -4.93 24.30493200 15.6 70 69.78692 0.21308 0.045403 -4.6 21.163500 15.89 71.885 75.30612 -3.42112 11.70408 -2.715 7.3712253700 16.338 72.32 78.09444 -5.77444 33.34412 -2.28 5.19843951 17.87 74.123 78.20029 -4.07729 16.62429 -0.477 0.2275293104 13.28 70.3 75.81442 -5.51442 30.40878 -4.3 18.493211 14 71.55 75.62538 -4.07538 16.60872 -3.05 9.30253623 15.33 73.115 79.94467 -6.82967 46.64441 -1.485 2.2052253737 16.11 74 79.69814 -5.69814 32.46877 -0.6 0.363508 15.87 73 75.55035 -2.55035 6.50428 -1.6 2.562915 13.32 71.108 71.55802 -0.45002 0.202522 -3.492 12.194062826 12.89 69.8 71.1243 -1.3243 1.753778 -4.8 23.042918 13.115 70 72.34072 -2.34072 5.478972 -4.6 21.163000 14.31 68.9 69.94502 -1.04502 1.092061 -5.7 32.493371 14.99 86 75.64587 10.35413 107.2079 11.4 129.963547 15 87.55 79.44416 8.10584 65.70464 12.95 167.70253725 15.67 90 80.97647 9.023531 81.42411 15.4 237.163111 13.8 80.5 74.14724 6.35276 40.35756 5.9 34.812715 12 75.89 71.8211 4.0689 16.55595 1.29 1.66412803 13.2 78.7 69.53858 9.16142 83.93162 4.1 16.81


3408 14.5 85.37 78.16639 7.20361 51.892 10.77 115.9929

97698 437.409 2238.263 0.625874 979.5158 0.263 1488.406

COORELATION TABLE OF TELENOR:

(X1 ) (X2) (Y) Ŷ=a+b1x1+b2x2 e=Y-Ŷ e² Y-Ý (Y-Ý)²2700 8.98 92 90.61842 1.38158 1.9087633 41 16812800 9 93.5 91.9247 1.5753 2.48157009 42.5 1806.251500 8.5 80 72.1077 7.8923 62.2883993 29 8412000 8.75 81 80.4112 0.5888 0.34668544 30 9002500 7 83 65.0867 17.9133 320.886317 32 1024

2508.85 6.5 70 59.274395 10.725605 115.038603 19 3612500 6.3 67 56.8169 10.1831 103.695526 16 2562900 5.5 65 51.6457 13.3543 178.337328 14 1962100 5.8 45 46.6299 -1.6299 2.65657401 -6 362000 5.3 41.85 39.6529 2.1971 4.82724841 -9.15 83.72252200 5.6 42 45.3371 -3.3371 11.1362364 -9 812800 5.75 43.75 53.5292 -9.7792 95.6327526 -7.25 52.56252600 5.98 40 54.10642 -14.10642 198.991085 -11 121

2709.8 6 41 55.51756 -14.51756 210.759548 -10 1002000 5.6 41 43.1971 -2.1971 4.82724841 -10 1002100 6 43.89 48.9927 -5.1027 26.0375473 -7.11 50.55212800 6.4 43 61.2083 -18.2083 331.542189 -8 642600 6.1 41 55.5241 -14.5241 210.949481 -10 1002200 6.05 40.77 50.6534 -9.8834 97.6815956 -10.23 104.65292400 6.09 41.65 53.26596 -11.61596 134.930527 -9.35 87.42251900 4 36 23.2247 12.7753 163.20829 -15 2252100 4.2 38 27.7275 10.2725 105.524256 -13 1691800 4.5 33 28.0617 4.9383 24.3868069 -18 3242000 4.6 34 31.3831 2.6169 6.84816561 -17 2892400 4.8 35 38.0259 -3.0259 9.15607081 -16 2562000 4.5 35 30.2017 4.7983 23.0236829 -16 2562400 5 38 40.3887 -2.3887 5.70588769 -13 1692400 5.1 40 41.5701 -1.5701 2.46521401 -11 1212700 5.4 54 48.3243 5.6757 32.2135705 3 9


2500 5.3 50 45.0029 4.9971 24.9710084 -1 1

70119 178.6 1529 1529.410955 -0.000955 2512.458 -0.59 9865.1625

Comparison of Correlation analysis with hypothesis:

Coefficient of determinationSST SSE SSR R2 Adjusted r2

Formulas

Jazz 1488 979 522 0.35 0.27Ufone 508.36 402.1382 106 0.21 0.12Telenor 9865.162 2512.458 7353 0.75 0.7448

Interpretation:

The “r” tells that which relation occurs between the two variables. Our calculated value of the “r” is showing that there is a strong positive relationship between Advertisement, No of customers and the Sims sold.R is the measure of correlation between the observed value and the predicted value of the criterion variable. In our project this would be the correlation between the no of Sims sold actual and no of Sims sold predicted by our predicted variables. R2: This is the measure of how good a prediction of the criterion variable we can make by knowing the predictor variable.r2 is the value of explained variation.What we forecasted through regression equation and find the error between actual value and predicted value we can reduce that error by explained variation.R2 tells us that how much percent our model is best fitted.SSR = Explained VariationSSE= Unexplained VariationBasically r2 tells us the accuracy of our model or how much the model is best fitted from the regression equation.


Ufone:

In our scenario of Ufone the value of r2 is 21% it means that Regression Equation can minimize 21% of the error or it has 21% explained variation, remaining 79% are the other factors are also included that are unexplained variation.

Adjusted r2

is the improved version of the r2

and it is over estimate the value so we find r2

which tells the error in Reality and it is always lesser than the r2

.

So, in our scenario value of adjusted r2

is 12% that means 12% error in the Regression

Equation in Reality.

Jazz:

In our scenario of Jazz the value of r2 is 35% it means that Regression Equation can minimize 35% of the error or it has 35% explained variation, remaining 65% are the other factors are also included that are unexplained variation.




Telenor:

In our scenario of Telenor the value of r2 is 75% it means that Regression Equation can minimize 75% of the error or it has 25% explained variation, remaining 25% are the other factors are also included that are unexplained variation.





Hypothesis of correlation of Ufone :

We want to check the relation between the Advertisement and the no of customers….

T-distribution

We reject h0 if t>2.05 and t<-2.05

We reject h0 because t>2.05 we are in the favor of H1 that means there is the Linear Relationship between the Dependent and the independent variable.


If Advertisement and the no of customers increase the no of Sims Sold will also increase.

Hypothesis of correlation of Jazz:

If there is a significant linear relationship between the independent variable and the

dependent variable, the value of will not equal zero.

T-distribution

We reject h0 if f>2.05 and f<-2.05


We reject h0 because f>2.05 we are in the favor of H1, so there is the Linear Relationship between the Dependent and the independent variable.If Advertisement and the no of customers increase the no of Sims Sold will also increase.

Hypothesis of correlation of Telenor:

If there is a significant linear relationship between the independent variable and the

dependent variable, the value of will not equal zero.

T-distribution

We reject h0 if t cal >2.05 and t<-2.05


. We reject h0 because t >2.05 we are in the favor of H1, so there is the Linear Relationship between the Dependent and the independent variable.If Advertisement and the no of customers increase the no of Sims Sold will also increase.

MULTIPLE REGRESSION OF UFONE:

X1 X2 y X1² X2² X1Y X2Y X1* X2 3832 6.75 44 14684224 45.5625 168608 297 258663730 6.16 42.55 13912900 37.9456 158711.5 262.108 22976.83500 7.12 41.8 12250000 50.6944 146300 297.616 249203808 5.11 39.31 14500864 26.1121 149692.48 200.8741 19458.883929 4.22 43.88 15437041 17.8084 172404.52 185.1736 16580.383708 4 40 13749264 16 148320 160 148323800 5.3 43.8 14440000 28.09 166440 232.14 201403513 5.05 40 12341169 25.5025 140520 202 17740.653617 5.711 42.9 13082689 32.615521 155169.3 245.0019 20656.6873615 6.15 41.67 13068225 37.8225 150637.05 256.2705 22232.25


3822 7.033 42.1 14607684 49.463089 160906.2 296.0893 26880.1264800 7.881 49.75 23040000 62.110161 238800 392.07975 37828.84908 8.1 52.81 24088464 65.61 259191.48 427.761 39754.84970 7.75 40.76 24700900 60.0625 202577.2 315.89 38517.53800 6.952 43.752 14440000 48.330304 166257.6 304.163904 26417.63600 6.509 40 12960000 42.367081 144000 260.36 23432.43700 6.8 40.71 13690000 46.24 150627 276.828 251604000 6 46 16000000 36 184000 276 240004325 7.22 48.751 18705625 52.1284 210848.075 351.98222 31226.54400 6.315 35 19360000 39.879225 154000 221.025 277864500 6.714 37 20250000 45.077796 166500 248.418 302134313 5.833 39.98 18601969 34.023889 172433.74 233.20334 25157.7294633 6.124 45.609 21464689 37.503376 211306.497 279.309516 28372.4924750 6.8 48 22562500 46.24 228000 326.4 323004831 7.81 50 23338561 60.9961 241550 390.5 37730.115000 7.65 42.66 25000000 58.5225 213300 326.349 382504625 4.553 40 21390625 20.729809 185000 182.12 21057.6254700 5.72 41.678 22090000 32.7184 195886.6 238.39816 268844653 5.209 44.749 21650409 27.133681 208217.097 233.097541 24237.4774900 3.88 50.909 24010000 15.0544 249454.1 197.52692 19012

126282 186.424 1300.128 539417802 1198.344232 5499658.439 8115.685751 789621.806

MULTIPLE REGRESSION OF Ufone:

This is our Regression Equation of Ufone.

INTERPRETATION:


is called the Multiple Regression equation.Y is the dependent variable or the predicted variable.

And x1 and x2 is the independent variable or it is called the predicted variable.X1= Advertisement (Independent Variable)X2= No of customers (Independent Variable)Y= Sims sold (Dependent Variable)

is the projected or forecasted value.We find the value of “e” because this value tells us the difference between the actual and the projected value.“a” is the fixed value and “b” is the per unit change, a = the y- intercept of the estimated plane which is the best point estimate of α.B 1 = the slope of the estimated plain with x1 which is the best point estimate of β1 and it is changed by the value of x1.B 2 = the slope of the estimated plain with x2 which is the best point estimate of β2 and it is changed by the value of x2.

The value of “a” tells us that if the x1 and x2 that is Advertisement and no of customers is zero then how much it is affected on the sale and it is fixed or constant so there is no change in the value of a when x1 or x2 is increase or decrease.B is the per unit change which change with the value of x1 and x2 that are our independent variables.

Now the regression equation is:Y = a + b1x1 + b2x2 + b3x3

In this equation Advertisement and the no of customers have the positive relation with no of Sims sold that means if Advertisement and no of customers will increase then no of Sims sold will also increase.


Hypothesis Testing of Multiple Regression of Ufone:

The test is used to check whether if there is a significant linear relationship between the

independent variable and the dependent variable, if yes then the slope will not equal zero.

HO: β0= 0H1: β0≠ 0

B1 = 0.003094


t tab. v, α = 27, 0.05 = 1.703288

We Reject H0 if t > 1.70.

T cal = 4.827

T > 1.70 so, we Reject H0, we are in the favor of H1 it means the value of b1 is not equal to the zero and there is the Perfect Relationship between the Advertisement and the no of Sims Sold.

B 2 = 0.536377

T tab. v, α = 27, 0.05 = 1.703288We Reject H0 if t > 1.70.

T cal = 1.243

T Cal< 1.70 so, we do not Reject H0, we are in the favor of H0 means the value of b2 is very nearly equal to the zero and there is no relationship between the No of customers and the no of Sims sold.

Multiple regressions ANOVA testing of Ufone:ScenarioAs the Analysis of the variance procedure is used to test the null hypothesis we want to check that the Advertisement and No of customers have direct relation or not means with the increase in the Advertisement no of customers will increase or not, or how much per unit change will occur on b1 with the change in b2, B1 Represents the Advertisement and B2 Represents the No of customers,Step1:

H0: b1 = b2

H1: b1 ≠ b2 Step2: F-Distribution


Step3:V1 = 3V2 = 26

If f cal is > 2.9752 then we reject H0 Otherwise we do not reject H0.

Step4:

Source of variation

Degree of freedom

Sum of squares (SS) Mean squares (MS)

Value of test statistics (f)

regression K -1= 2 SSR= 106 MSR=SSR/k = 53

F=MSR/MSE = 3.5584

Error n-k30- 3= 27

SSE= 402.1382 MSE=SSE/n-(k+1)= 14.894

Total 29 SST =508.36 MST=SST/n-1= 18

Step5:

Decision rule:

F cal is > 2.9752 so; we reject H0 we are in the favor of H1 that means with the increase in the Advertisement, no of customers do not increase.

REGRESSION TABLE OF JAZZ:

X1 X2 Y x1*x1 x1*Y x2*x2 x2*Y x1*x23751 15.339 85.551 14070001 320901.80 235.28 1312.27 57536.593432 14 81.33 11778624 279124.56 196.00 1138.62 48048.003000 14.53 78.555 9000000 235665.00 211.12 1141.40 43590.002721 13.88 65.33 7403841 177762.93 192.65 906.78 37767.483545 16.58 77.1 12567025 273319.50 274.90 1278.32 58776.103233 14.775 73.38 10452289 237237.54 218.30 1084.19 47767.582843 12.335 61.331 8082649 174364.03 152.15 756.52 35068.412900 13.557 64.523 8410000 187116.70 183.79 874.74 39315.30


3100 14.33 67.382 9610000 208884.20 205.35 965.58 44423.003300 15 69.67 10890000 229911.00 225.00 1045.05 49500.003200 15.6 70 10240000 224000.00 243.36 1092.00 49920.003500 15.89 71.885 12250000 251597.50 252.49 1142.25 55615.003700 16.338 72.32 13690000 267584.00 266.93 1181.56 60450.603951 17.87 74.123 15610401 292859.97 319.34 1324.58 70604.373104 13.28 70.3 9634816 218211.20 176.36 933.58 41221.123211 14 71.55 10310521 229747.05 196.00 1001.70 44954.003623 15.33 73.115 13126129 264895.65 235.01 1120.85 55540.593737 16.11 74 13965169 276538.00 259.53 1192.14 60203.073508 15.87 73 12306064 256084.00 251.86 1158.51 55671.962915 13.32 71.108 8497225 207279.82 177.42 947.16 38827.802826 12.89 69.8 7986276 197254.80 166.15 899.72 36427.142918 13.115 70 8514724 204260.00 172.00 918.05 38269.573000 14.31 68.9 9000000 206700.00 204.78 985.96 42930.003371 14.99 86 11363641 289906.00 224.70 1289.14 50531.293547 15 87.55 12581209 310539.85 225.00 1313.25 53205.003725 15.67 90 13875625 335250.00 245.55 1410.30 58370.753111 13.8 80.5 9678321 250435.50 190.44 1110.90 42931.802715 12 75.89 7371225 206041.35 144.00 910.68 32580.002803 13.2 78.7 7856809 220596.10 174.24 1038.84 36999.603408 14.5 85.37 11614464 290940.96 210.25 1237.87 49416.00

97698 437.409 2238.263 321737048 7325009.012 6429.959 32712.52 1436462

MULTIPLE REGRESSION OF JAZZ:

Interpretation:


In this equation Advertisement and the Sale has the positive relation and N o of customers and Sims sold has the negative relation that means If Advertisement will increase then no of Sims sold will also increase.

Hypothesis Testing of Multiple Regression of Jazz:

HO: β0= 0H1: β0≠ 0

B 1 = 0.02178

t tab. v, α = 27, 0.05 = 1.703288We Reject H0 if t > 1.70.

T cal = 14.449

T > 1.70 so, we Reject H0, we are in the favor of H1 that means Advertisement and no of Sims Sold have linear Relationship.

HO: β0= 0H1: β0≠ 0

B 2 = -3.4993

T cal = -48.3997


t > 1.70 So, we Do not Reject H0, we are in the favor of H0 that means No of customers and no of Sims sold have no relation.

Multiple regressions ANOVA testing of Jazz:ScenarioAs the Analysis of the variance procedure is used to test the null hypothesis that with the increase in Advertisement, no of customers will increase or not or how much per unit change will occur on b1 with the change in b2, B1 Represents the Advertisement and B2

Represents the No of customers,

Step1:H0: b1 = b2

H1: b1 ≠ b2 Step2:


F-DistributionStep3: V1 = 3

V2 = 26


Step4:

Source of variation

Degree of freedom

Sum of squares (SS) Mean squares (MS)


Regression K -1 = 2 SSR= 508 MSR=SSR/k = 254

F=MSR/MSE = 7.0006

Error n-k30-3= 27

SSE= 979 MSE=SSE/n-(k+1)= 36.25

Total 29 SST =1501 MST=SST/n-1= 51.75

Step5:

Decision:

f cal is > 2.9752 So, we reject H0, so we are in the favor of H1 so that Advertisement and no of customers have no relation to each other If we increase the Advertisement the no of customer will not increase

REGRESSION TABLE OF TELENOR:

(X1 ) (X2) (Y) (X1)2 (X2)2 X1Y X2Y X1X22700 8.98 92 7290000 80.6404 248400 826.16 242462800 9 93.5 7840000 81 261800 841.5 252001500 8.5 80 2250000 72.25 120000 680 127502000 8.75 81 4000000 76.5625 162000 708.75 175002500 7 83 6250000 49 207500 581 17500

2508.85 6.5 70 6294328.323 42.25 175619.5 455 16307.5252500 6.3 67 6250000 39.69 167500 422.1 157502900 5.5 65 8410000 30.25 188500 357.5 15950


2100 5.8 45 4410000 33.64 94500 261 121802000 5.3 41.85 4000000 28.09 83700 221.805 106002200 5.6 42 4840000 31.36 92400 235.2 123202800 5.75 43.75 7840000 33.0625 122500 251.5625 161002600 5.98 40 6760000 35.7604 104000 239.2 15548

2709.8 6 41 7343016.04 36 111101.8 246 16258.82000 5.6 41 4000000 31.36 82000 229.6 112002100 6 43.89 4410000 36 92169 263.34 126002800 6.4 43 7840000 40.96 120400 275.2 179202600 6.1 41 6760000 37.21 106600 250.1 158602200 6.05 40.77 4840000 36.6025 89694 246.6585 133102400 6.09 41.65 5760000 37.0881 99960 253.6485 146161900 4 36 3610000 16 68400 144 76002100 4.2 38 4410000 17.64 79800 159.6 88201800 4.5 33 3240000 20.25 59400 148.5 81002000 4.6 34 4000000 21.16 68000 156.4 92002400 4.8 35 5760000 23.04 84000 168 115202000 4.5 35 4000000 20.25 70000 157.5 90002400 5 38 5760000 25 91200 190 120002400 5.1 40 5760000 26.01 96000 204 122402700 5.4 54 7290000 29.16 145800 291.6 145802500 5.3 50 6250000 28.09 125000 265 13250

70119 178.6 1529 167467344 1115.4 420026.3 9729.9 420026.3

MULTIPLE REGRESSION OF TELENOR:

Interpretation:


In this equation Advertisement and the no of customers have the positive relation with no of Sims sold that means if Advertisement and no of customers will increase then no of Sims sold will also increase.

Hypothesis Testing of Multiple Regression of Telenor:

HO: β0= 0H1: β0≠ 0

B 1 = 0.0107


T cal = 14.069

T Cal > 1.70 so, we Reject H0, we are in the favor of H1 that means Advertisement and no of Sims Sold have linear Relationship.

HO: β0= 0H1: β0≠ 0

B 2 = 11.814



T cal = 40.320t Cal > 1.70 So, we Reject H0, we are in the favor of H1 that means no of customers and no of Sims Sold have linear Relationship.

Multiple regressions ANOVA testing of Telenor:Scenario

As the Analysis of the variance procedure is used to test the null hypothesis we want to check that the Advertisement and No of customers have direct relation or not means with the increase in the Advertisement no of customers will increase or not, or how much per unit change will occur on b1 with the change in b2, B1 Represents the Advertisement and B2 Represents the No of customers,

Step1:H0: B1 = B2

H1: B1 ≠ B2


Step2:F-Distribution

Step3:V1 = 3V2 = 26


Step4:

Source of variation

Degree of freedom

Sum of squares (SS)

Mean squares (MS)


regression K -1=2

SSR= 7353 MSR=SSR/k = 3676.5

F=MSR/MSE = 40

error n-k30-3= 27

SSE= 2512 MSE=SSE/n-(k+1)= 93.037

total 29 SST =9865 MST=SST/n-1= 340.17

Step5:Decision:

F cal is > 2.9752 so, we reject H0, and we are in the favor of H1 that is Advertisement and no of customers are not equal or with the increase in the Advertisement, no of customers will not increase.

Comparison among the companies on the basis of Regression and Correlation

UfoneIn this equation Advertisement and the no of customers have the positive relation with no of Sims sold that means if Advertisement and no of customers will increase then no of Sims sold will also increase.

T > 1.70 so, we Reject H0, we are in the favor of H1 it means the value of b1 is not equal to the zero and there is the Perfect Relationship between the Advertisement and the no of Sims Sold.


T Cal< 1.70 so, we do not Reject H0, we are in the favor of H0 means the value of b2 is very nearly equal to the zero and there is no relationship between the No of customers and the no of Sims sold.We reject h0 because t>2.05 we are in the favor of H1 that means there is the Linear Relationship between the Dependent and the independent variable.If Advertisement and the no of customers increase the no of Sims Sold will also increase.

JazzIn this equation Advertisement and the Sale has the positive relation and N o of customers and Sims sold has the negative relation that means If Advertisement will increase then no of Sims sold will also increase.

T > 1.70 so, we Reject H0, we are in the favor of H1 it means the value of b1 is not equal to the zero and there is the Perfect Relationship between the Advertisement and the no of Sims Sold.T Cal< 1.70 so, we do not Reject H0, we are in the favor of H0 means the value of b2 is very nearly equal to the zero and there is no relationship between the No of customers and the no of Sims sold.

We reject h0 because f>2.05 we are in the favor of H1, so there is the Linear Relationship between the Dependent and the independent variable.If Advertisement and the no of customers increase the no of Sims Sold will also increase.

TelenorIn this equation Advertisement and the no of customers have the positive relation with no of Sims sold that means if Advertisement and no of customers will increase then no of Sims sold will also increase.

T Cal > 1.70 so, we Reject H0, we are in the favor of H1 that means Advertisement and no of Sims Sold have linear Relationship.

t Cal > 1.70 So, we Reject H0, we are in the favor of H1 that means no of customers and no of Sims Sold have linear Relationship.


We reject h0 because t >2.05 we are in the favor of H1, so there is the Linear Relationship between the Dependent and the independent variable.If Advertisement and the no of customers increase the no of Sims Sold will also increase.

In Short

The Regression Equation of Ufone and the Telenor tells us that there is perfect relationship between the dependent and the independent variable but the Regression Equation of Jazz tells us that there is positive Relationship between the Advertisement and the no of Sims Sold but the negative relation between the no of customers and the no of Sims Sold

R2 Relationship Adjusted r2

Formulas

Jazz 0.35 Positive 0.27Ufone 0.21 Positive 0.12Telenor 0.75 Strong Positive 0.7448

It shows that the if Advertisement and no of Customers increases then the no of Sims Sold will also increase.

Crux of Study

We selected three companies for BQT project (jazz Ufone Telenor) and the three variables for each (two are the independent variables (Advertisement no of customers) and one is dependent (no of Sims sold). We applied different tools and came in to the following findings;Averagely advertisement of these three companies jazz, ufone and telenor are 3222000, 4156500 and 2400000 respectively. Ufone spend more money on advertisement than the jazz and telenor.The strength of the no of customers of jazz that is 15000 is greater than ufone and telenor.


Averagely no of Sims sold of jazz, ufone and telenor are 73000, 43000 and 41000 respectively. Sales of jazz is comparatively high because most of the people like business mans and households use the jazz network and also because of this reason that jazz is first telecommunication company in pakistan.there was large out layer in the data of telenor and when we apply median on the data than the average Sims sold of telenor is 4190 which is comparatively less than the sales of ufone.

Advertisements of ufone is more deviated from its mean than jazz and telenor because ufone focus and spend more on advertisement and no of customer of ufone are less deviated from its mean and sales of telenor are more deviated from its mean value. And coefficient of variance tells us these values in percentage.Skewness values of Advertisement of jazz and ufone shows that both are right skewed and advertisement of telenor is left skewed. No of customers of jazz and telenor is right skewed and no of customers of ufone is left skewed. Sales of these three companies are right skewed.In analysis of variance we checked that the mean of the variables of these companies are same or not (it does not mean that the values of the mean will be same it tells that the variation among the mean’s is less or more). After the Anova testing we found we are in the favor of H1 that the mean of all the companies are not the same.Then we did the correlation analysis with hypothesis testing. In our scenario of Ufone the value of r2 is 21% it means that Regression Equation can minimize 21% of the error.Value of adjusted r2 is 12% that means 12% error in the Regression Equation in Reality. And like this the value of the r2 and adjusted r2 for jazz are 35%, 27% and for telenor are 75%, 74%.

Recommendations and Suggestions

By increasing the advertisement the no of customers increased so we recommend to jazz that they should increase their advertisement so they can increase their no of customers as well as sales. Ufone should manage, develop and maintain productive relationship with internal customers and partners. Because of the Denmark issue the repute of Telenor was badly affected so the company should do their best to catch the customer’s loyalty they should offer attractive


packages and make more and attractive advertisement to catch customer’s attractiveness. Telenor Company can also increase their sales by enhancing their supply chain distribution.

Conclusions

The no of customers of the jazz are more than Ufone and Telenor because it was the first telecommunication company of the Pakistan and have the largest market share. Mostly the households and business people are using jazz due to their attractive offers like ladies first.The advertisement of the ufone is more than the jazz and telenor because with the increase of the advertisement they attract more customers as compare to the jazz and the telenor. Ufone is the second largest company in the Pakistan has also shown by the values of its customers and the no of Sims sold in the descriptive analysis.


The three companies we selected, from these three companies the telenor is at no. 3rd in every aspect as concluded from the values of Advertisement, no of customers, no of Sims sold.

Appendix


No Basic Calculations on

Excel

Any other Supporting material

Books of (Prem s Mann) (Anderson sweeni)


www.ufone.comwww.mobilinkgsm.comwww.telenor.com

http://www.telenor.com/

http://www.mobilinkgsm.com/

http://www.ufone.com/

final bqt report

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