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Example Questions
Example Question #2 : How To Use The Central Limit Theorem
If the Central Limit Theorem applies, we can infer that:
The sampling distribution will be approximately normally distributed.
The sample mean will be close to the population mean.
The standard deviation will be small.
Every possible sample mean will be equal to the population mean.
The sampling distribution will be approximately normally distributed.
The Central Limit Theorem tells us that when the theorem applies, the sampling distribution will be approximately normally distributed.
Example Question #3 : How To Use The Central Limit Theorem
A survey company samples 60 randomly selected college students to see if they own an American Express credit card. One percent of all college students own an American Express credit card. Does the Central Limit Theorem apply?
Yes.
Not enough information is given.
No.
No.
No. Whenever we get a "proportion" question we need to check whether 
In this problem, 
Therefore,
So the central limit theorem does not apply.
Example Question #13 : Sampling Distributions
In a particular library, there is a sign in the elevator that indicates a limit of 
If a random sample of 
None of the other answers
This question deals with the Central Limit Theorem, which states that a random sample taken from a large population where the sampling distribution of sample averages is approximately normal has a standard deviation equal to the standard deviation of the population divided by the square root of the sample size. The information given allows us to apply the Central Limit Theorem as it satisfies the necessary characteristics of the sampling distribution/size. The standard deviation of the population is 27lbs, and the sample size is 36; therefore, the standard deviation of the 36-person random sample is 
Example Question #13 : Sampling Distributions
The purpose of the t test is to do which of the following?
Compare the means of separate populations.
Describe the variance within a population.
Test the relationship between five or more variables.
Compare the significance of multiple variables.
Determine the ability of one variable to predict another.
Compare the means of separate populations.
A t test is used to compare the means of different groups. A t test score describes the likelihood that the difference in means between two groups is due to chance. The null hypothesis assumes the two sets are equal however, one can reject the null hypothesis with a p value within a particular confidence level.
Example Question #1 : Continuous Distributions
If 
Use the fact that the sum of 
Example Question #1 : Identifying Variables And Relationships
In a standard deck of cards, with replacement, what is the probability of drawing two Ace of Hearts?
Probability of drawing first ace of hearts:
Probability of drawing second ace of hearts:
Multiply both probabilities by each other:
Example Question #1 : How To Identify Independent Variables
A fair coin is flipped three times and comes up heads each time. What is the probability that the fourth toss will also come up heads?
Remember, no matter what the previous trials' results, the probability of a head (or tail) does not change from 
Example Question #1 : Dependence And Independence
What is the probability that a person will flip a coin and land on heads eight times in a row? Assume that the coin is fair and can either land on heads or tails.
1 out of 512
1 out of 64
1 out of 128
1 out of 256
It is not possible to flip a coin and land on heads ten times in a row.
1 out of 256
In this problem, it is important to identify that flipping a coin is an independent event. One coin flip does not affect the next coin flip. In each flip, there is a 50% chance of landing on heads and a 50% chance of landing on tails. By multiplying the probabilities with each other eight times, we get the following:
Example Question #1 : Independent Random Variable Combination
A dice is rolled four times in a row. What is the probability that every one of the four rolls will result in a result greater than 2? Assume the dice is fair and has six sides.
2 out of 3
75 out of 80
4 out of 6
25 out of 30
16 out of 81
16 out of 81
In this problem, each roll of the dice is independent from the previous roll. The probability of obtaining a result of greater than 2 on a single roll is 4/6 or 2/3 (since 3, 4, 5, and 6 are all greater than 2). In order to find the probability that this occurs four times in a row, we must multiply the following:
Example Question #1 : Dependence And Independence
Four people are playing a card game and each of them has their own 52-card deck that has been randomly shuffled.
What is the probability that all four of the individuals will draw a face card at random?
81 out of 28561
1 out of 52
12 out of 52
6000 out of 100000
9 out of 52
81 out of 28561
Since each deck of cards is separate and only one card is being drawn from each deck, the events are independent of each other. The probability of drawing a single face card (Jacks, Queens, or Kings) from a deck of cards is 12/52. We can multiply the probabilities as follows:
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