Answer:
Step-by-step explanation:
It would be A, the first one.
Answer:

Step-by-step explanation:
we know that
In the right triangle ABC
The function sine of angle 68 degrees is equal to divide the opposite side SB by the hypotenuse AC
so

substitute the values and solve for AC



Answer:
{∅, {a}, {b}, {a,b}}
Step-by-step explanation:
The value of power of a set is generalized by using the formula,
Power of a set (P) = 2^n where n is the number of element in the set.
Given two distinct elements a and b say;
A = {a,b}
n(A) = 2 i.e the number of elements in the set is 2. Therefore the power of the set will be 2^n which gives 2^2 = 4.
P(A) = 4 means there are 4 subsets of the given set. Subsets are sets of elements that can be found in the set. The subsets of element A will be;
{∅, {a}, {b}, {a,b}} which gives 4 elements in total.
Note that empty set ∅ is always part of the subset of any given set
Answer:
The correlation coeffcient for this case was provided:
r =0.934
And this coefficient is very near to 1 the maximum possible value, so then we can interpret that the relationship between the entrace exam score and the grade point average are strongly linearly correlated .
We can also find the
who represent the determination coefficient and we got:

And the interpretation for this is that a linear model explains appproximately 87.2% of the variability between the two variables
Step-by-step explanation:
Previous concepts
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.
And in order to calculate the correlation coefficient we can use this formula:
The correlation coeffcient for this case was provided:
r =0.934
And this coefficient is very near to 1 the maximum possible value, so then we can interpret that the relationship between the entrace exam score and the grade point average are strongly linearly correlated .
We can also find the
who represent the determination coefficient and we got:

And the interpretation for this is that a linear model explains appproximately 87.2% of the variability between the two variables