STATISTICAL ANALYSIS

Statistical Analysis

Statistical Analysis

Part 1

Descriptive Statistics

Mean

Std. Deviation

N

2011 January

4.9282E6

7.07783E6

27

2007 January

2.9647E6

4.35325E6

27

From the above descriptive table, it can be observed that the mean of number of broadband lines by country in the all the 2011 January and 2007 January are used in analyzing the number of broadband lines. The most key values of the above table are the means and the standard deviation of 2011January and 2007 January which are important to study as these values are showing the how much deviation is present in the number of broadband lines from 2011 January and 2007 January. From the above table, it is found that the standard deviation of 2011 January is high in comparison to the number of broadband lines 2007 January.

Correlations

2011 January

2007 January

Pearson Correlation

2011 January

1.000

.990

2007 January

.990

1.000

Sig. (1- tailed)

2011 January

.

.000

2007 January

.000

.

N

2011 January

27

27

2007 January

27

27

From the correlation table, it is reflected that the correlation is present between the number of broadband lines in 2007 January and 2011 January. The reason of this statement is that the significance value is less than 0.05 reflecting the presence of correlation. Moreover, in this context, the value of the Pearson correlation coefficient presents that the value is in positive that is 0.990 showing that the strong correlation exists between the number of broadband lines in 2007 January and 2011 January.

Model Summary b

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df 1

df 2

Sig. F Change

1

.990 a

.980

.979

1.01352E6

.980

1242.982

1

25

.000

a. Predictors: (Constant), 2007 January

b. Dependent Variable: 2011 January

The table of model summary is showing that the value of R-square is 0.980 and the value of the adjusted R-square is 97.9% that shows the strong relationship exists between the dependent variable that is number of broadband lines in 2011 January and the independent variable that is number of broadband lines in 2007 January, but it is not evident that the relationship of 2007 January and 2011 January is positive or negative. Besides it, the regression analysis is a statistical method based on studying the correlation between variables and is often used as a tool for the prediction. In the simplest case, we study the linear relationship between an independent variable or predictor and dependent variable (criterion) to determine if knowledge of the results for the first predicts, with a satisfactory degree of accuracy, the results that we should observe about the second (Aiken, West and Reno, 1991, 21-43). This prediction is made ??using an equation called regression equation, whose parameters are defined in terms of statistical characteristics that is averages, standard deviations, and correlation coefficient of two variables (Cohen, 2003, 16-27). Regression analysis has countless applications in humanities and social sciences (Keith, 2006, 20-36). In the field that interests us most, we should point out such that it can be used to study the predictive validity of an assessment instrument or measurement (Little & Rubin, 2002, 27-59).

ANOVA b

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1.277E15

1

1.277E15

1.243E3

.000 a

Residual

2.568E13

25

1.027E12

Total

1.302E15

26

a. Predictors: (Constant), 2007 January

b. Dependent Variable: 2011 January

The above chart is presenting that the significant value of above table is less than 0.05 which shows that the model is ...