Answer: (iv) 0.6

Calculation:

Mean Squared Error: $MSE = \frac{1}{n}\sum_{i=1}^n (y_i - \hat{y}_i)^2$

Given:
Predicted $\hat{y} = [2,3,5,4,6]$, Actual $y = [3,3,4,5,6]$, $n=5$

i	$y_i$	$\hat{y}_i$	$y_i-\hat{y}_i$	$(y_i-\hat{y}_i)^2$
1	3	2	1	1
2	3	3	0	0
3	4	5	-1	1
4	5	4	1	1
5	6	6	0	0
Sum squared error $=1+0+1+1+0=3$

$MSE = 3/5 = 0.6$

Hence (iv) 0.6 is correct. (0.2 would be MAE, not MSE).