1. What is the Central Limit Theorem? Try to state it in your
own words. 2. Consider the random variable x, where x is the number
of dots after rolling a die. Make a sketch of the probability
distribution of this variable. What is the expected value of x? 3.
Now consider the random variable that is the average number of dots
after four rolls. Is this variable normally distributed? Explain.
4. Suppose we changed the definition to the average...
I would like to see a proof of the Central Limit Theorem that
applies to a simple probability dice scenerio, say rolling a 6 x
amount of times. The goal is to help me understand the theorem with
a simple example. Thanks!
This week we’ve introduced the central limit theorem. According to
the central limit theorem, for all samples of the same size n with
n>30, the sampling distribution of x can be approximated by a
normal distribution.
In your initial post
use your own words to explain what this theorem means. Then provide
a quick example to explain how this theorem might apply in real
life. At last, please share with us your thoughts about why this
theorem is important.
For which of the following situations would the central limit
theorem not imply that the sample distribution for ?¯x¯ is
approximately Normal?
a population is not Normal, and we use samples of size ?=50n=50
.
a population is not Normal, and we use samples of size ?=6n=6
.
a population is Normal, and we use samples of size ?=50n=50
.
a population is Normal, and we use samples of size ?=6n=6 .
Use the Central Limit Theorem to calculate the following
probability. Assume that the distribution of the population data is
normally distributed. A person with “normal” blood pressure has a
diastolic measurement of 75 mmHg, and a standard deviation of 4.5
mmHg.
i) What is the probability that a person with “normal” blood
pressure will get a diastolic result of over 80 mmHg, indicating
the possibility of pre-hypertension?
ii) If a patient takes their blood pressure every day for 10
days,...
There are 4 conditions that must be true in order to use
the Central Limit theorem. 1) We must have a simple random sample
(SRS); 2) the sample size must be less than 10% of the population;
3) the observations must be independent; and 4) the sample size
must be large enough so that both np > 10 and n(1 - p) >10,
in which the true proportion (or probability) possessing the
attribute of interest is p. Then the Central...
Part 3: The Central Limit Theorem for a Sample
ProportionThere are 4 conditions that must be true in order to use
the Central Limit theorem. 1) We must have a simple random sample
(SRS); 2) the sample size must be less than 10% of the population;
3) the observations must be independent; and 4) the sample size
must be large enough so that both np > 10 and n(1 - p) >10,
in which the true proportion (or probability) possessing...