training data in supervised models, case of linear regresion
no. of features $p$ 
no. of observations (training examples) $n$


In your example, $p = 10$ and $n = 3000$

So, what you said is right, you have to perform the following equation 3000 times.
$$h(x) = \sum_{i=0}^p \theta_{i}x_{i
}$$

and, see that the summation runs from $0$ to $p$, so a total of $p+1$ multiplications, with the first one $\theta_{0}$ being bias, and $x_{0}$ being $1$ always. 

If you have all $\theta_{i}$ values, at inference time for getting prediction for each test example, you perform the equation above, but the objective of linear regression is to find the values of $\theta$. This is nothing but called as fitting the model.

And the equation for finding $\theta_{i}$ values is

$$\theta_{i} := \theta_{i} - \frac{\alpha}{n} \sum_{j=1}^{n}(h^{j}(x)- y^{j})x^{j}$$ 

You can observe that calculation of $\theta$ also has the first equation $h(x)$,

where $\alpha$ is the Learning rate.