Answer:
Step-by-step explanation:
Answer:
Se=1.2
Step-by-step explanation:
The standard error is the standard deviation of a sample population. "It measures the accuracy with which a sample represents a population".
The central limit theorem (CLT) states "that the distribution of sample means approximates a normal distribution, as the sample size becomes larger, assuming that all samples are identical in size, and regardless of the population distribution shape"
The sample mean is defined as:

And the distribution for the sample mean is given by:

Let X denotes the random variable that measures the particular characteristic of interest. Let, X1, X2, …, Xn be the values of the random variable for the n units of the sample.
As the sample size is large,(>30) it can be assumed that the distribution is normal. The standard error of the sample mean X bar is given by:

If we replace the values given we have:

So then the distribution for the sample mean
is:

We see it is the y terms squared so it opens left or right
in form
(y-k)^2=4(p)(x-h)
vertex is (h,k)
and p is distance from focus to vertex, also distance from vertex to directix
if p>0, then it opens to the right and dirextix is to the left of vertex
if p<0, then it opens to the left and directix is tothe right of vertex
so
(y-1)^2=4(4)(x-(-3))
vertex is (-3,1)
4>0 so dirextix is to left of vertex
left is in x direction
-3-4=-7
directix is x=-7