Answer: The standard deviation of the sampling distribution of M is equal to the standard deviation of the population divided by the square root of the sample size.
You can assume that the sampling distribution of M is normally distributed for any sample size.
Step-by-step explanation:
- According to the central limit theorem , if we have a population with mean
and standard deviation 
, then if we take a sufficiently large random samples from the population with replacement , the distribution of the sample means will be approximately normally distributed. - When population is normally distributed , then the mean of the sampling distribution = Population mean
- Standard deviation of the sampling distribution =
, where = standard deviation of the population , n= sample size.
So, the correct statements are:
- You can assume that the sampling distribution of M is normally distributed for any sample size.
- The standard deviation of the sampling distribution of M is equal to the standard deviation of the population divided by the square root of the sample size.
Answer:
see below
Step-by-step explanation:
1. m∠DEF = m∠_FEK___ = 44° = Def. of ∠ bisector
2. m∠DEK = m∠_DEF___+m∠FEK___= _angle addition postulate________________
3. m∠DEK = _44___ + _44___ = 88° = Substitution, Algebra
4. DE = _KE____ = Given
5. EF = _FE____ = Reflexive property
6. △DEF ≅ △_KEF____ = _SAS postulate________________
7. DF = FK_____ = ___CPCTC (corresponding parts of congruent triangles are congurent)______________
8. KF = 1/2DK =8 _____ = Algebra, 7
Answer:
The expression can be rewritten in 
form as following:
⇒ 
where 
Step-by-step explanation:
Given expression:
To rewrite the expression in the form of .
Solution:
By property of exponents :
<em>So, we can apply this property to the given expression.</em>
We have:
⇒
The above expression is in the form of where
