## The Statistical indicators of DSL Subscribers sample essay

The repeating sentences:

2nd repeat: United States and Japan are losing export market shares and new actors are emerging such as Hungary, Finland and Czech Republic. Page 3.

Change to Due to the fact that United States and Japan are losing a number of their export market shares, their Balassa index has subsequently decreased. In addition to this, Hungary, Finland, and Czech Republic, as new actors, has much potential with regard to their Balassa index.

The RCA index presents information about a country’s comparative advantage in ICT exports of goods and table below makes obvious each country’s gain or lost comparative advantage in ICT export through changes of RCA. RCA equivalent 1 indicates an identical export structure; RCA above 1 indicates relative specialization, while RCA below 1 means a country’s de-specialization. Page 4: kindly delete this paragraph because the term “RCA” has already been explained and there is no need to introduce this again.

Price is consistently a very important issue. Nowadays the supply side hat vast influence of price and it is one of the largest barriers to purchase of broadband service.

The Statistical Indicators:

In this section, descriptive statistics will be used to describe the characteristics of the data set regarding the DSL subscribers. Different statistical measures will be used and will be introduced in the following sections.

Measures of Central Tendency:

The value for the mean of the data set is equal to 3251010.03. This means that on the average, there are about 3251010.03 DSL subscribers in the OECD countries.

The Median of DSL Subscribers is equal to 1136709.50. This is the middle of the data set which means that half of the OECD countries have DSL subscribers above 1136709.50 and the other half has less than 1136709.50 DSL subscribers.

The computed value for the Mode of DSL Subscribers is equal to 60024.00. This means that a number of OECD countries has this much DSL subscribers, more specifically the countries of .

Measures of Variability:

The standard deviation describes the degree of spread in the data set. If the data lies close to the mean, then the standard deviation is small. The standard deviation for the number of DSL Subscribers is 4645295.15. It shows the amount of deviation from the mean (3251010.03). Apparently, the value of the standard deviation is far from the mean and this shows that the data set has a high degree of spread.

The largest number of DSL Subscribers is 19031154.00, which is in the United States, and the smallest number of DSL Subscribers is 60024.00, which is in Luxembourg. Computing for the range, which is the difference between the largest and the smallest value, the result is 18971130.00. This also indicates the degree of spread in the data set since the largest value and the smallest value is far from each other as indicated by the big difference in their range.

The value for the Skewness of the data set is 2.08. It means that the distribution of the number of DSL Subscribers is referred to an asymmetric tail extending out to the right or skewed to the right.

–Correlation between the number of DSL Subscribers and the Monthly Charge

In order to find the correlation between the Monthly Charge and the number of DSL Subscribers, the Pearson’s correlation is used.

The results show that the Pearson’s coefficient has a value of -0.355 for these two variables. It shows that the relation between Monthly Charge and the number of DSL Subscribers is negative and it implies inverse association. P-value is equal to 0.027. The P-value is smaller than 5%, which means that there is a correlation between the two factors but with the coefficient less than 0.5, the correlation is said to be weak.

The graph below shows the negative relation between the number of DSL subscribers and the Monthly charge. It means that countries with lower Monthly charge have more DSL subscribers. The distribution of countries in the graph is expansive and far from the correlation line. It reaffirms the previous conclusion that the correlation is not strong.

–Correlation between the number of DSL Subscribers and the Speed of Connection

The Pearson’s correlation is used to find a correlation between the number of DSL Subscribers and the Speed of Connection.

The Pearson’s coefficient is equal to 0.5. With the said value, it can be concluded that there is a positive correlation between the variables Speed of connection and the number DSL Subscribers and it implies direct association. The computed P-value is equal 0.002. Because this value is between 0 and 0.5, it means that there is a direct relation between the two factors where countries with higher connection speed have more DSL subscribers.

The graph below shows that there is direct relationship between the number of DSL subscribers and the Speed of Connection. It shows that in countries where the Speed of connection is higher, there are more DSL Subscribers.

–Correlation between the number of DSL Subscribers and the Monthly Charge and Speed of Connection

The possible value for Regression’s coefficient is between 0 and 1. If it is 0, then there is no correlation between factors of regression. If it is equal to 1, then the correlation is perfect. Also, the result of the analysis regression’s coefficient near 1 shows strong correlation.

The regression coefficient for measuring the correlation between two variables will be computed. These variables are the Monthly Charge and Speed of Connection as the independent variables and the dependent variable which is the DSL subscribers. The result shows that the regression coefficient value is equal to 0.574. It shows that there is a correlation between Monthly Charge and Speed of Connection with the number of DSL subscribers and this relation is .

There is another Index that will be used which is the R2. It is between 0% and 100%. It shows the percentage of change in the independent variables (Monthly charge and Speed of Connection) in relation to the change in dependent variable (number of DSL subscribers). It determines the influence that the independent variables (Charge and Speed) have over the change in the number of DSL Subscribers.

The R2 for the current variables is 0.330. It means that 33% of change in the number of DSL Subscribers can be attributed to Monthly Charge and Speed of Connection and rest is from other variables.

In addition to this, it is necessary to compute for the Beta Index. This Index determines the influence of each of the two independent variables, Monthly Charge and Speed of Connection. The value of Beta for Monthly charge is negative, which is -0.286. The value of Beta for Speed of Connection is 0.457. Upon a comparison of the value of Beta for the two independent variables, it could be said that the influence of Speed on the number of DSL Subscribers is more significant than the influence of Monthly Charge on the number of DSL Subscribers. In addition, the negative Beta Index of Monthly charge on DSL Subscribers means that as the Monthly Charge decreases, the number of DSL Subscribers increases. The graph below shows this regression.

Correlation between number of DSL Subscribers and the Monthly Charge in Middle East

The rate of Broadband penetration in Iran and Middle East is insignificant. The table below shows the Broadband penetration in Middle East.

-Correlation between the number of Broadband Subscribers and ICT Export in Middle East

The data on hand shows that the Pearson’s coefficient value of the number of Broadband Subscribers and ICT Export is equal to .939. It shows that the correlation between number of Broadband subscribers and ICT export is very strong. P-value is equal to 0.000, which means that there is a correlation between Broadband subscribers and ICT export.

The Regression’s Coefficient is 0.993 which means that the correlation of the two variables is strong since the value of 0.993 is close to 1. In addition to this, the R2 value is equal to 0.98. It means 98% of the change in the number of Broadband Subscribers in Middle East is related to ICT Export.

The graph below shows this regression.

The result can be considered for countries with available data. Due to the fact that there are not many countries with Broadband subscribers, the result cannot hold true for all Middle East countries.

-Correlation between number of DSL Subscribers and ICT Export in OECD

The variables number of DSL Subscribers and ICT Export in OCED countries is also subjected to the computation for Pearson’s correlation. The P-value is equal to 0.153, which is greater than 5%. It means that there is no correlation between DSL subscribers and ICT export in OECD countries.

The value for the Regression’s Coefficient is 0.267 which means that there is a correlation but it is not too strong. The R2 value for the variables is equal to 0.072. It means only 3% of the change in the number of Broadband Subscribers in OECD countries is related to ICT Export. It supports the idea that there is no correlation between DSL subscribers and ICT export.

The graph below shows this regression.

Iran is the second largest oil producer among the Oil Producing and Exporting Countries (OPEC). Furthermore, it has the world’s second largest reserves of natural gas.

This chapter analyses the ICT situation in Iran and it will evaluate the Internet and Mobile penetration in the Middle East region. The research tries to identify the obstacles for development of ICT in Iran and to serve as a basis in the proposal of new policies.

There are many active companies competing in the DSL arena in Iran. The major companies and their activities are listed below:

Almost all of the governments of Middle Eastern countries control the communication and information media services. Internet access is especially subjected to many restrictions. Some of the most important restrictions are (a) Religious restrictions, (b) Political restrictions, (c) Language restrictions, (d) Speed limitations, (e) Cost of service, (f) Technical problems (disruptions in connectivity). The reasons for most of these problems are the incompatible infrastructures, lack of skill for supporting services, scarcity of local websites, insufficiency for applied economy and life.

There are some other local obstacles in each country that complicates the infiltration of Internet access.

The table below shows the population and internet users. Moreover, the percentage of the total population with internet access will also be shown.

Figure below presents the number of internet users. Iran has the most number of internet users and Bahrain and Iraq have the least number of internet users. The comparison between internet penetration in 2000 and 2005 in this region shows that the growth of all countries is notable. The total growth of Internet users in Middle East from 2000 to the latest data available is 491.4%. Each country showed an increase in their rate of access to Internet by at least 100%.

The figure below displays the percentage of population with access to Internet.

The internet user share of population in Israel is the highest while the corresponding shares in Iraq and Yemen remained to be very low. As Figure 2 shows, 51% of Israel’s population have access to internet. United Arab Emirate with 36%, Qatar with 27%, Kuwait with 25%, and Bahrain with 21% followed Israel in the list. Other countries do not enjoy the same privileges where only a low percentage of the total population have access to the internet.

The share of Internet users in each country to the aggregate sum of Internet users in Middle East is calculated in the table below. Internet penetration and population of each country can influence the share of Internet users to the sum of Internet users in the Middle East. However, the table below demonstrates the level of interest of each country to communicate with other countries in economic and cultural aspects.

The results show that in 2005, Iran has almost 39% of Internet user in Middle East. Israel with 19%, Saudi Arabia with 13%, and United Arab Emirates with 7% has more contributions to assemble total Internet users in Middle East. Figure below displays the share of each country in total Internet users of Middle East.

The figure below shows correlation between Mobile population coverage and GDP per capita.

The Pearson’s correlation is used to find a correlation between GDP and Internet user, GDP and Mobile user, GDP and Fixed telephone.

The value of Pearson’s coefficients and the value of P-values show no relationship between GDP and three factors. The GDP does not have any influence in the penetration of Internet, Mobile, and Fixed telephone in the Middle East.

The graph below shows a negative relation between the number of DSL subscribers and Monthly charge. It means that countries with lower Monthly charge have more DSL subscribers. The distribution of countries in the graph is very wide and far from the correlation line, which means that correlation is weak.

The Pearson’s correlation is used to find a correlation between the variables Population and Internet Users.

The Pearson’s coefficient is equal 0.784. It shows that the relationship between Population and Internet users is positive and it implies direct association. P-value is equal to 0.001 and the value is between 0 and 0.5. It means there is a direct relation between two factors.

The graph below shows that there is a direct relationship between Population and Internet users. It shows that countries with more population tend to have more Internet users.

The Graph below shows this regression.

The Regression’s Coefficient is 0.784 and R2 is equal 0.614. It means 61% of the change in the number of Internet Users in Middle East countries is related to population.

The graph below shows the relation between all variables.

Relationship of each variable in the distribution’s Matrix is determined with other variables in Matrix through the row and column.

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