<image>The following chart shows the volume of harmless disposal of domestic waste (in hundreds of millions of tons) in China from 2008 to 2014 .

(1) From the line chart, it can be seen that a linear regression model can be used to fit the relationship between \( y \) and \( t \). Please use the correlation coefficient \( r \) to illustrate this;

(2) Establish the regression equation of \( y \) with respect to \( t \) (coefficients accurate to 0.01) and predict the volume of harmless disposal of domestic waste in China for the year 2016.

Notes:
Reference data: \( \sum\limits_{i=1}^{n}{\left( {{t}_{i}}-\overline{t} \right)\left( {{y}_{i}}-\overline{y} \right)}{=}2.89 \), \( \sqrt{\sum\limits_{i=1}^{7}{{{\left( {{y}_{i}}-\overline{y} \right)}^{2}}}}=0.55 \), \( \sqrt{7}\approx 2.646 \).

Reference formulas:
In the regression equation \( \widehat{y}=\widehat{a}+\widehat{b}t \), the least squares estimation formulas for the slope and intercept are:
\[ \widehat{a}{=}\overline{y}-\widehat{b}\overline{t} \]
\[ \widehat{b}=\dfrac{\sum\limits_{i=1}^{n}{\left( {{t}_{i}}-\overline{t} \right)\left( {{y}_{i}}-\overline{y} \right)}}{\sum\limits_{i=1}^{n}{{{\left( {{t}_{i}}-\overline{t} \right)}^{2}}}} \]

The correlation coefficient:
\[ r=\dfrac{\sum\limits_{i=1}^{n}{\left( {{t}_{i}}-\overline{t} \right)\left( {{y}_{i}}-\overline{y} \right)}}{\sqrt{\sum\limits_{i=1}^{n}{{{\left( {{t}_{i}}-\overline{t} \right)}^{2}}\sum\limits_{i=1}^{n}{{{\left( {{y}_{i}}-\overline{y} \right)}^{2}}}}}} \]