In order to do this, you must first find the "cross product" of these vectors. To do that, we can use several methods. To simplify this first, I suggest you compute:
‹1, -1, 1› × ‹0, 1, 1›
You are interested in vectors orthogonal to the originals, which don't change when you scale them. Using 0,-1,1 is much easier than 6s and 7s.
So what methods are there to compute this? You can review them here (or presumably in your class notes or textbook): http://en.wikipedia.org/wiki/Cross_produ...
In addition to these methods, sometimes I like to set up: ‹1, -1, 1› • ‹a, b, c› = 0 ‹0, 1, 1› • ‹a, b, c› = 0
That is the dot product, and having these dot products equal zero guarantees orthogonality. You can convert that to:
a - b + c = 0 b + c = 0
This is two equations, three unknowns, so you can solve it with one free parameter:
b = -c a = c - b = -2c
The computation, regardless of method, yields: ‹1, -1, 1› × ‹0, 1, 1› = ‹-2, -1, 1›
The above method, solving equations, works because you'd just plug in c=1 to obtain this solution. However, it is not a unit vector. There will always be two unit vectors (if you find one, then its negative will be the other of course). To find the unit vector, we need to find the magnitude of our vector:
Answer: Rejecting the mean weight of each cake as 500 gram when H subscript 0 equals 500
Step-by-step explanation:
Given that :
Null hypothesis : H0 =500
Alternative hypothesis : Ha < 500
Type 1 Error: Type 1 error simply occurs when we reject the Null hypothesis when the Null is true. Alternatively, type 11 error occurs when we fail to reject a false null hypothesis.
Hence, in the scenario above, a type 1 error will occur when we reject the mean weight as 500 even though the Null hypothesis is True.