Answer
0.6
Step-by-step explanation:
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Please help I don't understand
1 answer:
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Answer:
We want to find:
Here we can use Stirling's approximation, which says that for large values of n, we get:
Because here we are taking the limit when n tends to infinity, we can use this approximation.
Then we get.
Now we can just simplify this, so we get:
And we can rewrite it as:
The important part here is the exponent, as n tends to infinite, the exponent tends to zero.
Thus:
Answer:
1. Trapezoid
2. Kite
3. Square, Rhombus
Step-by-step explanation:
Given - Use the properties of quadrilaterals to complete the statements.
To find - A ..... has exactly one pair of parallel sides.
A ....... must have two pairs of adjacent sides congruent.
A ........ must have all congruent sides.
Proof -
A Trapezoid has exactly one pair of parallel sides.
A Kite must have two pairs of adjacent sides congruent.
A Square, Rhombus must have all congruent sides.
Reason -
- A trapezoid is a quadrilateral with exactly one pair of parallel sides.
- A kite is a quadrilateral with exactly two pairs of adjacent congruent sides.
- A rhombus is a parallelogram with four congruent sides.
- A square can be defined as a rhombus which is also a rectangle . In other words, a square is a parallelogram with four congruent sides and four right angles.
Answer:
a)From the central limit theorem we know that the distribution for the sample mean is given by:
And the standard error for the mean would be:
b) We want this probability:
And we can use the z score formula given by:
And replacing we got:
And we can find this probability with the complement rule and the normal standard deviation or excel and we got:
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 central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Solution to the problem
Part a
From the central limit theorem we know that the distribution for the sample mean is given by:
And the standard error for the mean would be:
Part b
We want this probability:
And we can use the z score formula given by:
And replacing we got:
And we can find this probability with the complement rule and the normal standard deviation or excel and we got: