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Factors Influencing the Diffusion of the Internet in China: 1997-2001
Unformatted Document Text:  15 number of computer per 100 people has the most significant impact on the output of internet population. Model 2 shows that adding of the number of schools in the model increase the explanatory power. In the third model, predictors from all the three factors are presented, and the Adjusted R square reaches 90.5%. __________________________ Table 6 about here __________________________ 3. Determinants of penetration rate Running factor analysis on all variables that have significant correlation with Internet penetration rate (Jan., 2001), only one factor can be retrieved (Table 7), which means all these variables are highly correlated to each other. In order to obtain a precise regression model and avoid multicollinearity (Tabachnick & Fidell, 2001), statistical regression is used again is used again. ---------------------------------------------------------- Table 7 about here --------------------------------------------------------- In four regression models with significant predictors, rate of population with higher education has the highest explanatory power, with largest Beta value in all models. Rate of home computer ownership, percentage of urban population and income of urban residents also contribute to explain the variance in adoption

Authors: Lin, Jia.
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15
number of computer per 100 people has the most significant impact on the output
of internet population. Model 2 shows that adding of the number of schools in the
model increase the explanatory power. In the third model, predictors from all the
three factors are presented, and the Adjusted R square reaches 90.5%.
__________________________
Table 6 about here
__________________________
3. Determinants of penetration rate
Running factor analysis on all variables that have significant correlation
with Internet penetration rate (Jan., 2001), only one factor can be retrieved
(Table 7), which
means all these variables are highly correlated to each other.
In order to obtain a precise regression model and avoid multicollinearity
(Tabachnick & Fidell, 2001), statistical regression is used again is used again.
----------------------------------------------------------
Table 7 about here
---------------------------------------------------------
In four regression models with significant predictors, rate of population
with higher education has the highest explanatory power, with largest Beta value
in all models. Rate of home computer ownership, percentage of urban population
and income of urban residents also contribute to explain the variance in adoption


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