Answer:
The answer is the option A
A straight line passing through the origin
Step-by-step explanation:
we know that
A relationship between two variables, x, and y, represent a proportional variation if it can be expressed in the form or
In a proportional relationship the constant of proportionality k is equal to the slope m of the line and the line passes through the origin
Answer:
Step-by-step explanation:
1. The measure of an inscribed angle is always half the measure of the arc it forms. Since angle ACB forms arc AB with a measure of 100 degrees, the measure of angle ACB will be equal to .
2. Relating to problem 1, both inscribed angles marked in the figure form the same arc. All inscribed angles forming the same arc will have the same measure. Therefore, the measure of angle GEF is equal to .
Answer:
Step-by-step explanation:
can go into
twice at the most, so doubling
will give us
, which leaves us with
left over to reach 315, which is why
is the numerator. The remainder is ALWAYS the numerator.
I am joyous to assist you anytime.
Answer:
And we can solve this using the following z score formula:
And if we use this formula we got:
So we can find this probability equivalently like this:
Step-by-step explanation:
Previous concepts
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".
Solution to the problem
Let X the random variable that represent the heights of a population, and for this case we know the distribution for X is given by:
Where and
We select n =100. Since the distribution for X is normal then we know that the distribution for the sample mean is given by:
We want this probability:
And we can solve this using the following z score formula:
And if we use this formula we got:
So we can find this probability equivalently like this: