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[ASSESS]

[ADVANCE]

[VERIFY]

[SIMPLIFY]

[SYNTHESIZE]

[PIVOT]

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In recent years, the technological level of new energy vehicles in China has been continuously improving, with significant performance enhancements. The production and sales scale has ranked first in the world for six consecutive years. The "Development Plan for the New Energy Vehicle Industry (2021-2035)" proposes that by 2025, the sales volume of new energy vehicles will reach around 20% of the total sales volume of new vehicles. It aims to achieve the international advanced level in core technologies of new energy vehicles after 15 years of continuous efforts, with quality brands possessing strong international competitiveness. The statistical data of a certain automobile city's consecutive operating days $x$ and the total sales volume of new energy vehicles $y$ (unit: vehicles) from a certain day are shown in the table below:

| Consecutive Operating Days $x$ | 10 | 20 | 30 | 40 | 50 |
|-------------------------------|----|----|----|----|----|
| New Energy Vehicle Sales Total $y$ | 62 | 68 | 75 | 81 | 89 |

$(Ⅰ)$ It is known that a linear regression model can be used to fit the relationship between $y$ and $x$. Please explain using the correlation coefficient $r$ (results accurate to $0.001$).

$(Ⅱ)$ Find the linear regression equation of $y$ with respect to $x$, $\hat{y}=\hat{b}x+\hat{a}$, and predict the total vehicle sales of the automobile city operating continuously for 130 days.

Reference data: $\sum_{i=1}^{5}x_{i}y_{i}=11920$, $\sum_{i=1}^{5}{{y}_{i}}^{2}=28575$, $\sqrt{5}\approx 2.236$.

Reference formulas: Correlation coefficient $r=\frac{\sum_{i=1}^{n}{x}_{i}{y}_{i}-n\overline{x}•\overline{y}}{\sqrt{\sum_{i=1}^{n}{x}_{i}^{2}-n{\overline{x}}^{2}}\sqrt{\sum_{i=1}^{n}{y}_{i}^{2}-n{\overline{y}}^{2}}}$

Regression line $\hat{y}=\hat{b}x+\hat{a}$, where the least squares estimates of the slope and intercept are:

$\hat{b}=\frac{\sum_{i=1}^{n}({x}_{i}-\overline{x})({y}_{i}-\overline{y})}{{\sum_{i=1}^{n}({x}_{i}-\overline{x})^{2}}}=\frac{\sum_{i=1}^{n}{x}_{i}{y}_{i}-n\overline{x}\overline{y}}{{\sum_{i=1}^{n}{{x}_{i}}^{2}-n(\overline{x})^{2}}}$

$\hat{a}=y-\hat{b}x$

When $|r| \gt 0.75$, there is a strong linear relationship between the two variables.

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