Among, Big-O, Big-Omega and Big-Theta, Indicate the efficiency class of a linear search.
The best case (Big-O) for a linear search would be, 1 (or constant) because the item being looked for, could be the first in the list.
The worst case (Big-Omega) for a linear search would be, n (or linear) because it could be the last item in the list of n items.
However, what would the efficiency class of Big-Theta (average case) be for a linear search?
The average case estimate depends on the statistical distribution of the data, so is not uniquely defined.
In the uniform case (all elements equally likely to be a match, and probability of a match being $p$),
with probability $p$ you perform between $1$ and $n$ comparisons (equally weighted),
with probability $1-p$ you perform $n$ comparisons.
In total,
$$p\frac{n+1}2+(1-p)n=\left(1-\frac p2\right)n+\frac p2$$
which is $\Theta(n)$.