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Since December 2022, various regions in the country have been issuing multiple rounds of consumption vouchers in different forms to boost consumption recovery. Let the amount of issued consumption vouchers be denoted as $x$ (in hundreds of million yuan) and the consumption driven be denoted as $y$ (in hundreds of million yuan). The data of some randomly sampled cities in a province are shown in the table below.

| $x$ | 3 | 3 | 4 | 5 | 5 | 6 | 6 | 8 |
|-----|---|---|---|---|---|---|---|---|
| $y$ | 10| 12| 13| 18| 19| 21| 24| 27|

$(1)$ Based on the data in the table, explain with the correlation coefficient that $y$ and $x$ have a strong linear relationship, and find the linear regression equation of $y$ with respect to $x. 

$(2)$ 
- $(i)$ If city $A$ in the province plans to issue a round of consumption vouchers with an amount of 10 hundred million yuan in February 2023, using the linear regression equation obtained in $(1)$, how much consumption is expected to be driven?
- $(ii)$ When the absolute difference between the actual value and the estimated value is not more than 10% of the estimated value, it is considered ideal for the issued consumption vouchers to boost consumption recovery. If after issuing consumption vouchers with an amount of 10 hundred million yuan in February in city $A$, a statistical analysis after one month reveals that the actual consumption driven is 30 hundred million yuan, is the boost in consumption recovery ideal? If not, analyze possible reasons.

Reference formulas: 
$r=\frac{{\sum_{i=1}^n{({{x_i}-\overline{x}})({{y_i}-\overline{y}})}}}{{\sqrt{\sum_{i=1}^n{{{({{x_i}-\overline{x}})}^2}}\sum_{i=1}^n{{{({{y_i}-\overline{y}})}^2}}}}}$, $\hat{b}=\frac{{\sum_{i=1}^n{({{x_i}-\overline{x}})({{y_i}-\overline{y}})}}}{{\sum_{i=1}^n{{{({{x_i}-\overline{x}})}^2}}}}$, $\hat{a}=\overline{y}-\hat{b}\overline{x}$. When $|r| > 0.75$, there is a strong linear relationship between the two variables.

Reference data: $\sqrt{35} \approx 5.9$.

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