factors affecting internet development - twisted...

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Factors Affecting Internet Development

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Prepared For:

Dr. Abdul H. Chowdhury

Business Statistics

BUS 511

MBA Program, Summer 2009

Prepared By:

Shahpar Sultana ID # 092 0526 060

Sabria Afrin ID # 092 0424 060

Mohaimeen Kamal ID # 092 0594 560

MBA Program

School of Business

North South University, Bangladesh

7th September, 2009

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7th September, 2009 Dr. Abdul H. Chowdhury Course Instructor Business Statistics BUS 511 MBA Program, School of Business North South University

Subject: Submission of Project Report

Dear Sir,

It is our great honor to submit the report of our project report “Factors Affecting Internet

Development”. In this endeavor, this report seeks to identify and analyze the relationships

among the variables. The report contains executive summary, statistical analysis and some

findings and recommendations. It would be our enormous pleasure if you find this report useful

and informative to have an apparent perspective on the issue.

Thanking you.

Yours sincerely,

Shahpar Sultana ID # 092 0526 060

Sabria Afrin ID # 092 0424 060

Mohaimeen Kamal ID # 092 0594 560

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A Report of Business Statistics [BUS‐511]

Approved by:

Dr. Abdul H. Chowdhury

MBA Program, School of Business

North South University

Prepared by:

Shahpar Sultana

ID # 092 0526 060



Sabria Afrin

ID # 092 0424 060



Mohaimeen Kamal

ID # 092 0594 560



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ACKNOWLEDGEMENTS

First of all we like to express our sincere gratitude to almighty Allah that we have successfully

completed our report.

We feel pleased to have the opportunity of expressing our heart‐felt and most sincere gratitude

to our instructor, Prof. Dr. Abdul H. Chowdhury for his excellent guideline which mentored us

the way to prepare this project report, for his constant supervision, valuable advice, continual

encouragement and extraordinary patience, without which this report would have not been

possible.

Finally, we would like to thanks our class mates for their co‐operative attitude which guide us to

recover the problems regarding our report.

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EXECUTIVE SUMMARY

Today, the Internet is increasingly making its presence felt, not only playing an important role in

research and education but also serving as a catalyst to a country’s socio–economic, cultural

and political development. It is therefore not a surprise that the Internet has become a

development of the highest significance.

The purpose of this study is to find out the various factors affecting the internet development,

like GDP per capita, Urban Population, Literacy Rate, Telephone & Mobile user, Electricity

Consumption per capita, Percentage of Educational Expenditure of GDP & Political Stability.

There is a real danger that the global information society will remain global in name only if no

assistance is rendered to poorer countries. While financial assistance is important to Internet

growth, it may not be the sole factor that determines ICT development in a country. Therefore,

it is important for us to understand what other factors may facilitate Internet development.

With a better understanding of the various factors affecting Internet diffusion, it is hoped that

developing countries will better target their efforts in reducing the digital divide and make the

Internet a truly global information network.

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Table of Content

Title Page No Acknowledgement V Executive Summary Vi Chapter 1 9 1.1 Origin of the report 10 1.2 Problem Statement 10 1.3 Need for the study 11 1.4 Objectives of the study 11 1.5 Flow Chart of workings 12 Chapter 2 14 2.1 Introduction 14 2.2 Energy Crisis 15 2.3 Emerging shortages 15 2.4 National GDP 15 2.5 Energy and Population 16 2.6 Energy and Poverty 17 2.7 Energy per capita and vehicles per capita 18 2.8 Energy per capita and Oil Consumption 18 2.9 Population Means 18 Chapter 3 19 3.1 Theoretical Model 20 3.2 Regression Model 20 3.3 Hypothesis 21 3.4 Secondary Sources of Data 22 Chapter 4 23 4.1 Data 24 4.2 Descriptive Statistics 25 4.3 Regression 28 4.4 Test of Hypothesis 31 4.5 Scatter Diagram 33 Chapter 5 36 Bibliography 38

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List of Tables

Title Page No Data 24 Descriptive Statistics 1 25 Descriptive Statistics 2 28 Model Summary 28 Correlations Statistics 31

List of Graphs Title Page No GDP vs Energy per capita 25 Unemployment rate vs Energy per capita 26 Oil Production vs Energy per capita 26 Oil consumption vs Energy per capita 27 Vehicles per capita vs Energy per capita 27 Scatter Diagram Energy per capita vs Vehicles per capita 33 Scatter Diagram Energy per capita vs Oil consumption 33 Scatter Diagram Energy per capita vs Oil production 34 Scatter Diagram Energy per capita vs Unemployment rate 34 Scatter Diagram Energy per capita vs GDP 35

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Chapter 1

INTRODUCTION

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1.1 Origin of the Report

BUS 511 is a statistics course offered in the MBA program of NSU in order to equip students

with the statistical tools. The project was initiated so that the students would get a practical

exposure of statistical analysis in a project work.

1.2 Problem Statement

Today, the Internet is increasingly making its presence felt, not only playing an important role in

research and education but also serving as a catalyst to a country’s socio–economic, cultural

and political development. It is therefore not a surprise that the Internet has become a

development of the highest significance.

Here in this paper a model is to be set up to establish the relationship between internet user

rate and some related variables like GDP per capita, Urban Population, Literacy Rate, Telephone

& Mobile user, Electricity Consumption per capita, Percentage of Educational Expenditure of

GDP & Political Stability.

The digital divide boosted by contemporary communication technologies (primarily by the

Internet revolution) is fast raising concerns among nations in the developing world as it allows

some individuals or nations to benefit more from the use of such technologies than others. As a

result, efforts are being made by some of the poorer countries to catch up with technology by

pumping resources into communication hardware and software. However, these efforts so far

have not achieved much in bridging the gaps in ICT development among various countries.

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1.3 Need for the study

There is a real danger that the global information society will remain global in name only if no

assistance is rendered to poorer countries. While financial assistance is important to Internet

growth, it may not be the sole factor that determines ICT development in a country. Therefore,

it is important for us to understand what other factors may facilitate Internet development.

With a better understanding of the various factors affecting Internet diffusion, it is hoped that

developing countries will better target their efforts in reducing the digital divide and make the

Internet a truly global information network. The findings of factors affecting internet

development with relationship between various factors would indicate some significant

scenario how does it differ.

1.4 Objectives of the study

The main objective of this research is to estimate the relationships between Rate of Internet

user and some related variables like GDP per capita, Urban Population, Literacy Rate,

Telephone & Mobile user, Electricity Consumption per capita, Percentage of Educational

Expenditure of GDP & Political Stability. So this research is intended‐

To find out the level of impact of GDP per capita, Urban Population, Literacy Rate,

Telephone & Mobile user, Electricity Consumption per capita, Percentage of Educational

Expenditure of GDP & Political Stability on Rate of Internet user.

To get a practical exposure of statistical analysis including Descriptive statistics,

Regression analysis, Correlation coefficient and Test of hypotheses along with level of

significance in a project work.

To interpret several parameter values.

Policy recommendations.

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1.5 Flow Chart of workings

Study area

↓

Collecting data

↓

Secondary sources

↓

Journals & Reports, Web sites

↓

Data

Compilation

↓

Data Processing

& Analyzing

↓

Major findings corresponding with Objectives Applicable steps

↓

Final reports

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Chapter 2

GENERAL BACKGROUND

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2.1 Introduction

The Digital Age. The Computer Age. The Information Era. These are but three names that are

used to describe the current age, and they are completely accurate. Although there are still

some older people that refuse to embrace the internet, most young people could not even

imagine life without it. We use computers to shop, organize and print our photos, and to

research all different kinds of information, among others.

The discovery of the Internet technology has paved the way to a lot of major developments in

life. Internet has played a very significant role in the improvements of many industries

especially in the field of information technology and business development.

Internet has become a common business tool used by almost all companies today. Internet has

become a necessity. The need for Internet has grown very fast during the past few years. In no

time, the every single household will be having its own Internet connection.

2.2 National GDP

Growth in the production of goods and services is a basic determinant of how the economy

fares. By allocating total production to each unit of population, the extent to which the rate of

individual output contributes to the development process can be measured. It indicates the

pace of per capita income growth and also the rate that resources are used up. As a single

composite indicator of economic growth, it is a most powerful summary indicator of the

economic state of development in its many aspects. It does not directly measure sustainable

development but it is a very important measure for the economic and developmental aspects

of sustainable development, including people's consumption patterns and the use of renewable

resources.

GDP Per Capita refers to the gross domestic product per person. GDP includes personal

consumption, investments, government spending and exports that a country makes. The total

values of imports are subtracted from the total.

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2.3 Literacy Rate

The literacy rate is the percentage of people who can read in a certain country. There are no

universal definitions and standards of literacy. Unless Specified. All rates are based on the most

common definition – the ability to read and write at specified age. Information on Literacy,

while not perfect educational result, is probably the most easily available and valid for

international comparison.

2.4 Percentage of Urban Population

The data used here percentage of urban population describes the percentage of the total

population living in urban areas, as defined by country.

2.5 Telecommunication Structure

Here the telecommunication structure is a measure of percentage of total number of telephone

and mobile user, of total population.

2.6 Electricity Consumption Per Capita

This is the measure of country wise how much electricity consumed in KW per capita.

2.7 Educational Expenditure

Data used here as a educational expenditure represent how much the spending on educational

purpose of GDP on percentage basis.

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2.8 Political Stability

Country wise there are many political conditions. Some countries have democracy, some have

republican government, some have monarchy and some have military junta.

According to our study we denote value – “1” for democratic or republican government; and

value – “0” for monarchy and military junta.

2.9 Population Means

Mean of GDP Per Capita = 20736

Mean of Urban Population(%) = 66.02

Mean of Telephone & Mobile user(%) = 108.63

Mean of Literacy rate(%) = 89.17

Mean of Electricity Consumption per capita = 5666 KW

Mean of Internet User(%) = 37.47

Mean of Educational Expenditure (% of GDP) = 5.57

Mean of Political Stability = .78

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Chapter 3

Statistical Approaches

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3.1 Theoretical Model:

Internet User =ƒ (GDP/capita, Urban Population, Telephone & Mobile user, Literacy Rate,

Electricity Consumption/capita, Educational Expenditure, Political Stability)

3.2 Regression Model:

A multiple regression equation was drawn as follows on the basis of Least Square Method:

Ŷ = β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7

Where,

Ŷ = estimated percentage of internet user

x1 = GDP per capita

x2 = Urban Population

x3 = Telephone & Mobile User

x4 = Literacy rate

x5 = Electricity Consumption per capita

x6 = Educational Expenditure

x7 = Political Stability

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3.3 Hypothesis

H1: The higher a country’s GDP per capita, the more likely that it has a higher Internet penetration.

H2: The higher the literacy rate of a country’s population, the higher the internet user.

H3: Greater urban population tends to associate with more Internet user.

H4: A well–established telecommunication infrastructure in a country tends to associate with a high Internet penetration.

H5: The higher the Electricity Consumption per capita of a country’s, the higher percentage of the internet user.

H6: Greater Education Spending of GDP (% of GDP) tends to associate with more Internet user.

H7: A Stable Political Condition in a country tends to associate with a high Internet penetration.

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3.4 Secondary Sources of Data

Considering time and other limitations, the authors found that it would be most appropriate to

work with the Central Intelligence Agency database as available in website addressed

www.cia.gov and also from a statistical website addressed www.nationmaster.com

Number of Observation: 50

Variables:

Dependent:

• Internet User (% of total population of a country)

Independent:

• GDP/Capita

• Literacy Rate

• Urban Population (% of total population of a country)

• Telephone & Mobile user (% of total population of a country)

• Electricity Consumption per Capita ( in KW)

• Educational Expenditure (% of GDP)

• Political Stability

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Chapter 4

Workings

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4.1 Data:

Serial Number

Country Internet User (%) GDP(PPP) Per Capita,

($) Urban

Population (%) Telephone &

Mobile User (%) 1 Australia 52.86 38100 89 145.89 2 Cuba 11.44 9500 76 10.84 3 Yemen 1.34 2400 31 16.57 4 Brunei 51.40 53100 75 107.94 5 Denmark 63.63 37400 87 164.84 6 Malaysia 61.71 15300 70 107.70 7 Sweden 77.27 38500 85 175.25 8 Norway 81.54 55200 77 154.06 9 Namibia 4.79 5400 37 44.50 10 Kenya 7.69 1600 22 30.01 11 New Zealand 79.75 27900 87 141.24 12 Finland 68.57 37200 63 148.94 13 Belgium 50.12 37500 97 143.05 14 Bolivia 10.23 4500 66 40.23 15 Jamaica 53.08 7400 53 100.39 16 Iceland 65.96 39900 92 174.18 17 Switzerland 60.62 40900 73 172.21 18 Portugal 33.14 22000 59 163.92 19 United States 72.59 47000 82 136.13 20 United Kingdom 65.78 36600 90 172.92 21 Austria 52.09 39200 67 160.07 22 Poland 41.58 17300 61 134.41 23 France 48.85 32700 77 144.01 24 Hungary 42.40 19800 68 144.17 25 Ukraine 21.88 6900 68 149.01 26 South Africa 10.40 10000 61 95.70 27 Mexico 20.51 14200 77 79.14 28 Thailand 20.36 8500 33 88.61 29 Bhutan 5.79 5600 35 25.94 30 Canada 83.61 39300 80 118.70 31 Colombia 26.51 8900 74 91.75 32 Iran 34.62 12800 68 80.69 33 Italy 55.05 3100 68 181.43 34 Zimbabwe 11.86 200 37 13.79 35 Omen 9.95 20200 72 80.98 36 Germany 51.62 34800 74 183.29 37 Spain 48.59 34600 77 166.31 38 Hong Kong 56.14 43800 100 204.46 39 South Korea 73.37 26000 81 138.95 40 Brazil 25.16 10100 86 80.70 41 India 6.86 2800 29 34.31 42 Argentina 22.75 14200 92 121.97 43 Russia 21.43 15800 73 152.77 44 Turkey 17.12 12000 69 104.67 45 Japan 69.33 34200 66 124.78 46 Nepal 1.18 1100 17 6.73 47 Philippines 5.41 3300 65 56.57 48 Pakistan 9.93 2600 36 52.52 49 Bangladesh 0.32 1500 27 22.79 50 Indonesia 5.41 3900 52 41.48

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Serial Number

Country Literacy Rate

(%) Electricity

Consumption/capita

Education Expenditure. (%

of GDP) Political Stability

1 Australia 97.2 10720.76 4.9 1 2 Cuba 99.8 130.47 18.7 0 3 Yemen 50.2 175.83 9.5 1 4 Brunei 92.7 7671.30 9.1 0 5 Denmark 99 6707.16 8.5 0 6 Malaysia 88.7 3724.98 8.1 0 7 Sweden 99 14769.40 7.7 0 8 Norway 100 24011.23 7.6 0 9 Namibia 85 1557.43 7.2 1 10 Kenya 85.1 145.80 7 1 11 New Zealand 99 9436.72 6.7 1 12 Finland 100 16850.37 6.4 1 13 Belgium 99 8157.77 6.3 1 14 Bolivia 86.7 558.39 6.3 1 15 Jamaica 87.9 2290.02 4.9 1 16 Iceland 99 31147.29 6 1 17 Switzerland 99 7897.63 5.8 1 18 Portugal 93.3 4584.67 5.8 1 19 United States 99 12924.22 5.7 1 20 United Kingdom 99 5773.62 5.3 1 21 Austria 98 7566.72 5.7 1 22 Poland 99.8 3311.26 5.6 1 23 France 99 7328.28 5.6 1 24 Hungary 99.4 3690.24 5.5 1 25 Ukraine 99.4 3905.85 5.4 1 26 South Africa 86.4 5486.63 5.3 1 27 Mexico 91 1858.31 5.3 1 28 Thailand 92.6 1914.27 5.2 0 29 Bhutan 47 227.16 5.2 0 30 Canada 99 16279.41 5.2 1 31 Colombia 90.4 868.82 5.2 1 32 Iran 77 2160.44 4.9 1 33 Italy 98.4 5400.28 4.7 1 34 Zimbabwe 90.7 885.66 4.7 1 35 Omen 81.4 4013.81 4.6 0 36 Germany 99 6662.91 4.6 1 37 Spain 97.9 5834.17 4.5 0 38 Hong Kong 93.5 5748.14 4.4 0 39 South Korea 97.9 7515.58 4.2 1 40 Brazil 88.6 2116.72 4.2 1 41 India 61 466.03 4.1 1 42 Argentina 97.2 2497.93 4 1 43 Russia 99.4 6968.57 3.8 1 44 Turkey 87.4 1940.08 3.7 1 45 Japan 99 7701.96 3.6 1 46 Nepal 48.6 70.87 3.4 1 47 Philippines 92.6 556.10 3 1 48 Pakistan 49.9 430.18 1.8 1 49 Bangladesh 47.9 148.05 2.4 1 50 Indonesia 90.4 496.32 1.2 1

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Political Stability

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4.2 Test of Hypothesis for Mean

1. GDP Per Capita Mean (x’) = 20736, Standard Deviation (S) = 16208, n = 50 Ho : µ = 16000 HA : µ ≠ 16000 Test Statistic: z = x’ ‐ µo / s √ n With α = .039 Hence Reject the Null Hypothesis Ho

2. Internet User Mean (x’) = 37.47, Standard Deviation (S) = 26.29, n = 50 Ho : µ = 25 HA : µ ≠ 25 Test Statistic: z = x’ ‐ µo / s √ n With α = .001 Hence Reject the Null Hypothesis Ho

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3. Urban Population

Mean (x’) = 66.02, Standard Deviation (S) = 20.91, n = 50 Ho : µ = 50 HA : µ ≠ 50 Test Statistic: z = x’ ‐ µo / s √ n With α = .000 Hence Reject the Null Hypothesis Ho

4. Telephone & Mobile User Mean (x’) = 108.63, Standard Deviation (S) = 55.71, n = 50 Ho : µ = 75 HA : µ ≠ 75 Test Statistic: z = x’ ‐ µo / s √ n With α = .000 Hence Reject the Null Hypothesis Ho

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5. Literacy Rate Mean (x’) = 89.17, Standard Deviation (S) = 15.45, n = 50 Ho : µ = 65 HA : µ ≠ 65 Test Statistic: z = x’ ‐ µo / s √ n With α = .000 Hence Reject the Null Hypothesis Ho

6. Electricity Consumption(KW) Per Capita Mean (x’) = 5666, Standard Deviation (S) = 6259, n = 50 Ho : µ = 3000 HA : µ ≠ 3000 Test Statistic: z = x’ ‐ µo / s √ n With α = .003 Hence Reject the Null Hypothesis Ho

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7. Educational Expenditure (% of GDP)

Mean (x’) = 5.57, Standard Deviation (S) = 2.53, n = 50 Ho : µ = 4 HA : µ ≠ 4 Test Statistic: z = x’ ‐ µo / s √ n With α = .000 Hence Reject the Null Hypothesis Ho

8. Political Stability Mean (x’) = 0.78, Standard Deviation (S) = 0.4185, n = 50 Ho : µ = 0 HA : µ ≠ 0 Test Statistic: z = x’ ‐ µo / s √ n With α = .000 Hence Reject the Null Hypothesis Ho

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4.3 Confidence Interval

For 95% Confidence Interval the range of the variables is shown in table below:

Name of Variable Range

Internet User (%) 30.18 – 44.76

GDP Per Capita 16243 – 25229

Urban Population (%) 60.22 – 71.82

Telephone & Mobile User (%) 93.19 – 124.07

Literacy Rate 84.89 – 93.45

Electricity Consumption(KW)/capita 3931 – 7401

Educational Expenditure(% of GDP) 4.869 – 6.271

Political Stability 0.664 – 0.896

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Minitab Output

One-Sample Z: GDP_Per_capita Test of mu = 16000 vs not = 16000 The assumed standard deviation = 16208 Variable N Mean StDev SE Mean 95% CI Z P GDP_Per_capita 50 20736 16208 2292 (16243, 25229) 2.07 0.039 One-Sample Z: Internet_User Test of mu = 25 vs not = 25 The assumed standard deviation = 26.29 Variable N Mean StDev SE Mean 95% CI Z P Internet_User 50 37.47 26.29 3.72 (30.18, 44.76) 3.35 0.001 One-Sample Z: Urban_population Test of mu = 50 vs not = 50 The assumed standard deviation = 20.91 Variable N Mean StDev SE Mean 95% CI Z P Urban_population 50 66.02 20.91 2.96 (60.22, 71.82) 5.42 0.000 One-Sample Z: Tel_&_Mobile_User Test of mu = 75 vs not = 75 The assumed standard deviation = 55.71 Variable N Mean StDev SE Mean 95% CI Z P Tel_&_Mobile_User 50 108.63 55.71 7.88 (93.19, 124.07) 4.27 0.000 One-Sample Z: Literacy_Rate Test of mu = 65 vs not = 65 The assumed standard deviation = 15.45 Variable N Mean StDev SE Mean 95% CI Z P Literacy_Rate 50 89.17 15.45 2.18 (84.89, 93.45) 11.06 0.000

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One-Sample Z: Elec._Consmp_per_cap Test of mu = 3000 vs not = 3000 The assumed standard deviation = 6259 Variable N Mean StDev SE Mean 95% CI Z P Elec._Consmp_per_cap 50 5666 6259 885 (3931, 7401) 3.01 0.003 One-Sample Z: Education_Exp. Test of mu = 4 vs not = 4 The assumed standard deviation = 2.53 Variable N Mean StDev SE Mean 95% CI Z P Education_Exp. 50 5.570 2.530 0.358 (4.869, 6.271) 4.39 0.000 One-Sample Z: Political_Stability Test of mu = 0 vs not = 0 The assumed standard deviation = 0.4185 Variable N Mean StDev SE Mean 95% CI Z Political_Stability 50 0.7800 0.4185 0.0592 (0.6640, 0.8960) 13.18 Variable P Political_Stability 0.000

39

4.4 Descriptive Statistics

Table.1: Basic Descriptive Statistics

Entry Name N Minimum Maximum Mean Std. Deviation

Internet User (%) 50 0.32 83.61 37.47 26.29

GDP Per Capita ( US $)

50 200.00 55200.00 20736.00 16208.00


50 17.00 100.00 66.02 20.91


50 6.73 204.46 108.63 55.71

Literacy Rate 50 47.00 100.00 89.17 15.45

Electricity Consumption Per capita

50 71.00 31147.00 5666.00 6259.00

Educational Expenditure (% of GDP)

50 1.20 18.70 5.57 2.53

Political Stability 50 0.00 1.00 0.78 0.42

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Table.2: Extensive Descriptive Statistics

Entry Name

Internet User (%)

GDP Per Capita ( US $)

Urban Population

(%)


Literacy Rate



Political Stability

N Valid 50 50 50 50 50 50 50 50

Missing 0 0 0 0 0 0 0 0

Mean 37.47 20736 66.02 108.63 89.17 5666 5.57 0.78

Median 38.10 15550 69.50 120.33 95.35 3960 5.25 1

Mode ‐ 14200 68,77 ‐ 99 ‐ 5.2 1

Stnd. Error of Mean

3.72 2292 2.96 7.88 2.19 885 0.36 0.06

Variance 690.94 262693371 437.16 3103.63 238.83 39177165 6.40 0.17

Coefficient of Variance

70.15 78.16 31.67 51.28 17.33 110.47 45.42 53.65

Skewness 0.13 0.41 ‐0.75 ‐0.37 ‐1.92 2.11 2.89 ‐1.39

Kurtosis ‐1.40 ‐1.20 ‐0.25 ‐1.09 2.61 5.61 14.31 ‐0.06

Quartile 1 10.36 5550 57.50 55.56 87.23 881 4.35 1

Quartile 3 60.89 37250 80.25 153.09 99.00 7593 6.30 1

41

MINITAB OUTPUT

Descriptive Statistics: Internet_Use, GDP_Per_capi, Urban_popula, ... Total Variable Count N N* Mean SE Mean StDev Variance Internet_User 50 50 0 37.47 3.72 26.29 690.94 GDP_Per_capita 50 50 0 20736 2292 16208 262693371 Urban_population 50 50 0 66.02 2.96 20.91 437.16 Tel_&_Mobile_User 50 50 0 108.63 7.88 55.71 3103.63 Literacy_Rate 50 50 0 89.17 2.19 15.45 238.83 Elec._Consmp_per_cap 50 50 0 5666 885 6259 39177165 Education_Exp. 50 50 0 5.570 0.358 2.530 6.401 Political_Stability 50 50 0 0.7800 0.0592 0.4185 0.1751 Variable CoefVar Sum of Squares Minimum Q1 Median Q3 Internet_User 70.15 104063.49 0.32 10.36 38.10 60.89 GDP_Per_capita 78.16 34371060000 200 5550 15550 37250 Urban_population 31.67 239353.00 17.00 57.50 69.50 80.25 Tel_&_Mobile_User 51.28 742103.22 6.73 55.56 120.33 153.09 Literacy_Rate 17.33 409249.04 47.00 87.23 95.35 99.00 Elec._Consmp_per_cap 110.47 3524697731 71 881 3960 7593 Education_Exp. 45.42 1864.870 1.200 4.350 5.250 6.300 Political_Stability 53.65 39.0000 0.0000 1.0000 1.0000 1.0000 Variable Maximum Skewness Kurtosis Internet_User 83.61 0.13 -1.40 GDP_Per_capita 55200 0.41 -1.20 Urban_population 100.00 -0.75 -0.25 Tel_&_Mobile_User 204.46 -0.37 -1.09 Literacy_Rate 100.00 -1.92 2.61 Elec._Consmp_per_cap 31147 2.11 5.61 Education_Exp. 18.700 2.89 14.31 Political_Stability 1.0000 -1.39 -0.06 N for Variable Maximum Mode Mode Skewness Kurtosis Internet_User 83.61 * 0 0.13 -1.40 GDP_Per_capita 55200 14200 2 0.41 -1.20 Urban_population 100.00 68, 77 4 -0.75 -0.25 Tel_&_Mobile_User 204.46 * 0 -0.37 -1.09 Literacy_Rate 100.00 99 12 -1.92 2.61 Elec._Consmp_per_cap 31147 * 0 2.11 5.61 Education_Exp. 18.700 5.2 4 2.89 14.31 Political_Stability 1.0000 1 39 -1.39 -0.06

42

0

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Yemen

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Internet User (%)

GDP(PPP) Per Capita, Thousand($)

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Internet User (%)


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Literacy Rate

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Internet User (%)

Political Stability

46

4.5 Regression Analysis

The regression equation is

Internet User = ‐ 13.2 + 0.000629 GDP Per capita ‐ 0.022 Urban population + 0.160 Telephone & Mobile User + 0.125 Literacy Rate + 0.000893 Electricity Consumption per capita + 0.626 Educational Expenditure + 2.69 Political Stability

From this equation these are point out that:

• For a single unit change of GDP per capita, the Internet user will be changed 0.000629 units, and the variables share a positive relationship to each other.

• For a single unit change of Urban Population, the Internet user will be changed 0.022 units, and the variables share a negative relationship to each other.

• For a single unit change of Telephone & Mobile User, the Internet user will be changed 0.160 units, and the variables share a positive relationship to each other.

• For a single unit change of Literacy rate, the Internet user will be changed 0.125 units, and the variables share a positive relationship to each other.

• For a single unit change of Electricity Consumption per capita, the Internet user will be changed 0.000893 units, and the variables share a positive relationship to each other.

• For a single unit change of Educational Expenditure, the Internet user will be changed 0.626 units, and the variables share a positive relationship to each other.

• For a single unit change of Political Stability, the Internet user will be changed 2.69 units, and the variables share a positive relationship to each other.

47

Predictor Coef SE Coef T P

Constant ‐13.23 13.15 ‐1.01 0.320

GDP Per capita 0.0006291 0.0002384 2.64 0.012

Urban population ‐0.0224 0.1563 ‐0.14 0.887

Tel. & Mobile User 0.16001 0.06773 2.36 0.023

Literacy Rate 0.1246 0.2060 0.60 0.549

Elec. Consumption per capita

0.0008926 0.0004740 1.88 0.067

Education Exp. 0.6260 0.9379 0.67 0.508

Political Stability 2.687 5.396 0.50 0.621

S = 13.2379 R‐Sq = 78.3% R‐Sq(adj) = 74.6%

The coefficient of determination (R2) and the adjusted value was found to be 78.3% and 74.6% respectively. That Means the Internet User can be explained 78.3% by GDP Per capita, Urban population, Telephone & Mobile User, Literacy Rate, Electricity Consumption per capita, Educational Expenditure & Political Stability.

As because, we cannot except p value greater than 0.05, so;

The revised regression equation is,

Internet User = ‐ 13.2 + 0.000629 GDP per capita + 0.160 Telephone & Mobile User + 0.000893 Electricity Consumption per capita

48

MINITAB OUTPUT

Regression Analysis: Internet_Use versus GDP_Per_capi, Urban_popula, ... The regression equation is Internet_User = - 13.2 + 0.000629 GDP_Per_capita - 0.022 Urban_population + 0.160 Tel_&_Mobile_User + 0.125 Literacy_Rate + 0.000893 Elec._Consmp_per_cap + 0.626 Education_Exp. + 2.69 Political_Stability Predictor Coef SE Coef T P Constant -13.23 13.15 -1.01 0.320 GDP_Per_capita 0.0006291 0.0002384 2.64 0.012 Urban_population -0.0224 0.1563 -0.14 0.887 Tel_&_Mobile_User 0.16001 0.06773 2.36 0.023 Literacy_Rate 0.1246 0.2060 0.60 0.549 Elec._Consmp_per_cap 0.0008926 0.0004740 1.88 0.067 Education_Exp. 0.6260 0.9379 0.67 0.508 Political_Stability 2.687 5.396 0.50 0.621 S = 13.2379 R-Sq = 78.3% R-Sq(adj) = 74.6% Analysis of Variance Source DF SS MS F P Regression 7 26496.1 3785.2 21.60 0.000 Residual Error 42 7360.1 175.2 Total 49 33856.3 Source DF Seq SS GDP_Per_capita 1 23270.9 Urban_population 1 581.8 Tel_&_Mobile_User 1 1666.4 Literacy_Rate 1 188.1 Elec._Consmp_per_cap 1 700.8 Education_Exp. 1 44.6 Political_Stability 1 43.5 Unusual Observations Obs GDP_Per_capita Internet_User Fit SE Fit Residual St Resid 2 9500 11.44 17.04 10.80 -5.60 -0.73 X 6 15300 61.71 31.51 4.73 30.19 2.44R 11 27900 79.75 52.62 3.32 27.13 2.12R 15 7400 53.08 25.06 3.00 28.02 2.17R 16 39900 65.96 84.27 9.89 -18.30 -2.08RX R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage.

49

4.6 Correlation Statistics

Table 3: Correlation Statistics – Correlation Coefficient (R)

Internet User

GDP Per

Capita

Urban Population

Telephone & Mobile User

Literacy Rate



Political Stability

Internet User 1

GDP Per Capita

0.829 1

Urban Population

0.662 0.683 1

Telephone & Mobile User

0.789 0.745 0.731 1

Literacy Rate 0.629 0.566 0.731 0.701 1


0.741 0.757 0.515 0.611 0.471 1


0.126 0.175 0.162 ‐0.060 0.251 0.11 1

Political Stability

‐0.14 ‐2.82 ‐0.142 ‐0.081 ‐0.034 ‐0.097 ‐0.43 1

50

MINITAB OUTPUT

Correlations: Internet_Use, GDP_Per_capi, Urban_popula, Tel_&_Mobile, ... Internet_User GDP_Per_capita Urban_population GDP_Per_capita 0.829 0.000 Urban_population 0.662 0.683 0.000 0.000 Tel_&_Mobile_Use 0.789 0.745 0.731 0.000 0.000 0.000 Literacy_Rate 0.629 0.566 0.731 0.000 0.000 0.000 Elec._Consmp_per 0.741 0.757 0.515 0.000 0.000 0.000 Education_Exp. 0.126 0.175 0.162 0.385 0.225 0.262 Political_Stabil -0.140 -0.282 -0.142 0.331 0.048 0.326 Tel_&_Mobile_Use Literacy_Rate Elec._Consmp_per Literacy_Rate 0.701 0.000 Elec._Consmp_per 0.611 0.471 0.000 0.001 Education_Exp. -0.060 0.215 0.111 0.677 0.133 0.441 Political_Stabil -0.081 -0.034 -0.097 0.578 0.815 0.503 Education_Exp. Political_Stabil -0.430 0.002

51

4.6 Scatter Plot

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_Use

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_Use

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_Use

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_Use

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_Use

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54

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rScatterplot of Internet_User vs Political_Stability

55

4.7 Hypothesis Test for Correlation

Hypothesis 1: The higher a country’s GDP per capita, the more likely that it has a higher Internet penetration. There is a relationship between GDP per capita & Internet user.

Ho : ρ = 0 HA : ρ ≠ 0 Test Statistic: here, r = 0.829 n = 50 α = .012

Hence Reject the Null Hypothesis Ho

Hence Hypothesis 1 is established.

Hypothesis 2: The higher the literacy rates of a country’s population, the higher the internet user. There is a relationship between literacy rates & Internet user.


Hence We Fail to Reject the Null Hypothesis Ho

Hence Hypothesis 2 is not established.

Hypothesis 3: Greater urban population tends to associate with more Internet user. There is a relationship between urban population & Internet user.




56

Hypothesis 4: A well–established telecommunication infrastructure in a country tends to associate with a high Internet penetration. There is a relationship between Total Telephone and Mobile user & Internet user.




Hypothesis 5: The higher the Electricity Consumption per capita of a country’s, the higher percentage of the internet user. There is a relationship between Electricity Consumption per capita & Internet user.




Hypothesis 6: Greater Education Spending of GDP (% of GDP) tends to associate with more Internet user. There is a relationship between Education Spending & Internet user.




57

Hypothesis 7: A Stable Political Condition in a country tends to associate with a high Internet penetration. There is a relationship between Political Condition & Internet user.

Ho : ρ = 0 HA : ρ ≠ 0 Test Statistic: here, r = ‐ 0.14 n = 50 α = .621


Hence Hypothesis 7 is not established

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Normal Probability Plot(response is Internet_User)

Figure: Normal Probability Plot for Residuals

58

Chapter 5

Findings & Recommendations

59

It is clear from the analysis and statistical data that GDP per capita, Literacy rate, Urban

population, A well established telecommunication structure, Electricity consumption per capita

all these factors have more or less have some impacts on the significance of internet user.

Percentage of GDP spending on Educational purpose & Political stability don’t have any

significant affect on internet user rate according tour sample data of 50 countries.

In the end of regression analysis, correlation & some other statistical tests we found that GDP

per capita, well established telecommunication structure & electricity consumption per capita

have very considerable affect on the rate of internet user in a country.

The change in GDP per capita & Electricity consumption per capita is not possible in easily for

developing & under developed countries. But in very low cost primary level investment of

people can make the telecommunication structure very strong. As an example for our country

Bangladesh there are lots of mobile operator gives us chance to get connected to internet via

very cheap mobile handset.

So we must recommend putting more emphasis on telecommunication structure for some

rapid change on internet development for developing & under developed countries.

The research was done with a number of limitations. There may be other factors apart from the

other identified variables that can affect energy per capita. The research could have been

better if other variables could have been studied. The research was carried out in a limited time

and as a result detailed study was not possible although there were good intentions.

60

Bibliography

61

Websites:

www.cia.gov

www.nationmaster.com

http://ausweb.scu.edu.au/proceedings/boalch/paper.html ("A Preliminary model of

Internet diffusion within developing countries" by B. Bazar and G. Boalch)

http://outreach.lib.uic.edu/www/issues/issue9_2/hao/index.html (“Factors affecting

Internet development : An Asian survey by Hao Xiaoming and Chow Seet Kay”)

factors affecting internet development - twisted...

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