Can anyone help me with this question.?
What term is being described?
Choose from these answers:
(A)Central Limit Theorem
(B)Normal distribution
(C)Standard Error
(D)Z-score
(E)Transformation rules
1 If you keep taking more and more samples, the average of the samples' Measurement of something will get closer and closer to the true average of the full population's measure of it. ?
2 Convert a raw score into how many standard deviations it is from the mean?
http://www.jiskha.com/display.cgi?id=1429908496
I did not understand that answer. Ms. Sue
It doesn't take much to figure this one out...
In probability theory, the central limit theorem (CLT) states that, given certain conditions, the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined expected value and well-defined variance, will be approximately normally distributed, regardless of the ...
Central limit theorem - Wikipedia, the free encyclopedia
en.wikipedia.org/wiki/Central_limit_theoremWikipedia
Math - Anonymous, Friday, April 24, 2015 at 5:09pm
Whats for the part 2? bobpursley
Math - Damon, Friday, April 24, 2015 at 6:05pm
Z score
Sure! I can help you with that.
For the first question, the term being described is the (A) Central Limit Theorem. The Central Limit Theorem states that as you keep taking more and more samples from a population, the average of the samples' measurements will converge towards the true average of the population. In other words, it explains how the sampling distribution of the mean approaches a normal distribution, regardless of the shape of the population distribution.
To get to this answer, you can eliminate options (B) Normal distribution, (C) Standard Error, (D) Z-score, and (E) Transformation rules, as they do not directly match the description provided. Then, you are left with option (A) Central Limit Theorem, which fits the explanation.
For the second question, the term being described is the (D) Z-score. A Z-score is used to convert a raw score (an individual data point) into the number of standard deviations it is away from the mean of the distribution. It indicates how far a particular value is from the mean in terms of standard deviation units.
To arrive at this answer, you can easily identify the term by recognizing that the description refers to converting a raw score into standard deviations from the mean. This matches the definition of a Z-score.
I hope this helps! If you have any more questions, feel free to ask.