The regression coefficient was calculated to measure the correlation between the two variables namely, monthly charge and speed of connection, on the dependent variable which is the volume of DSL subscription. Regression coefficient is a constant which represents the rate of change of one variable, which in this dissertation is the volume of DSL subscription, as a function of changes in the other variables, namely monthly charge and speed of connection (Kachigan, 1991).

Descriptive statistics were used to explain the statistical indicators of the following variables : (a) DSL monthly charge in OECD member countries, (b) DSL speed of connection in OECD member-countries, and (c) volume of DSL subscription in the light of the problem under study. The descriptive measures involved the following three major characteristics of each the above-mentioned variables : distribution, central tendency and the dispersion or variability. The distribution summarizes the frequency of individual values or ranges of values for each variable.

The central tendency of the distribution is an estimate of the center of a distribution of values. Three major types of estimates of central tendency are the mean, which is simply the sum of all the individual values divided by the number of values; the median, which is the score found at the middle of the values arranged either from highest to lowest or lowest to highest; and the mode which is the most frequently occurring value in the set of data (Trochim, 2006; Sternstein, 2005).

Dispersion, on the other hand refers to the spread of the values around the central tendency, measured either using the range and the standard deviation. The range is simply the difference between the highest and lowest values in the distribution; whereas the standard deviation shows the relation that a set of values has to the mean of the sample, its numerical value indicating how one specific value in the distribution is clustered around or is scattered from the mean of the distribution (Trochim, 2006; Freund & Williams, 1983).

Methodology for Chapter 4: Pearson Correlation and Descriptive Statistics In Chapter 4, the Pearson correlation was utilized to find the degree to which the following pairs of variables are linearly associated : (a) GDP and Internet usage, GDP and mobile phone usage and GDP and fixed phone usage (b) population and Internet usage in Middle East, (c) population and mobile usage in Middle East, and (d) population and fixed phone usage in Middle East.

Interpretation of the specific relationships between the aforementioned pairs of variables was facilitated using Table 1 on page 2. Descriptive statistics were used to discuss the statistical indicators of the following variables : (a) fixed telephones in the 30 provinces of Iran, (b) data network capacity in 30 provinces of Iran, and (c) mobile penetration in 30 provinces of Iran.

The same statistical descriptors outlined in Chapter 3 were adopted for Chapter 4.

REFERENCES Asian Development Bank. (2007). Aggregate Measures of Competitiveness. Retrieved December 31, 2007, from adb. org: http://www. adb. org/documents/books/ADO/2003/part3_3-3. asp. Easton, V. J. , & McColl, J. H. (2004). Statistics Glossary: Paired data, correlation & regression. Retrieved December 31, 2007, from Statistical Education Through Problem Solving

Descriptive statistics were used to explain the statistical indicators of the following variables : (a) DSL monthly charge in OECD member countries, (b) DSL speed of connection in OECD member-countries, and (c) volume of DSL subscription in the light of the problem under study. The descriptive measures involved the following three major characteristics of each the above-mentioned variables : distribution, central tendency and the dispersion or variability. The distribution summarizes the frequency of individual values or ranges of values for each variable.

The central tendency of the distribution is an estimate of the center of a distribution of values. Three major types of estimates of central tendency are the mean, which is simply the sum of all the individual values divided by the number of values; the median, which is the score found at the middle of the values arranged either from highest to lowest or lowest to highest; and the mode which is the most frequently occurring value in the set of data (Trochim, 2006; Sternstein, 2005).

Dispersion, on the other hand refers to the spread of the values around the central tendency, measured either using the range and the standard deviation. The range is simply the difference between the highest and lowest values in the distribution; whereas the standard deviation shows the relation that a set of values has to the mean of the sample, its numerical value indicating how one specific value in the distribution is clustered around or is scattered from the mean of the distribution (Trochim, 2006; Freund & Williams, 1983).

Methodology for Chapter 4: Pearson Correlation and Descriptive Statistics In Chapter 4, the Pearson correlation was utilized to find the degree to which the following pairs of variables are linearly associated : (a) GDP and Internet usage, GDP and mobile phone usage and GDP and fixed phone usage (b) population and Internet usage in Middle East, (c) population and mobile usage in Middle East, and (d) population and fixed phone usage in Middle East.

Interpretation of the specific relationships between the aforementioned pairs of variables was facilitated using Table 1 on page 2. Descriptive statistics were used to discuss the statistical indicators of the following variables : (a) fixed telephones in the 30 provinces of Iran, (b) data network capacity in 30 provinces of Iran, and (c) mobile penetration in 30 provinces of Iran.

The same statistical descriptors outlined in Chapter 3 were adopted for Chapter 4.

REFERENCES Asian Development Bank. (2007). Aggregate Measures of Competitiveness. Retrieved December 31, 2007, from adb. org: http://www. adb. org/documents/books/ADO/2003/part3_3-3. asp. Easton, V. J. , & McColl, J. H. (2004). Statistics Glossary: Paired data, correlation & regression. Retrieved December 31, 2007, from Statistical Education Through Problem Solving