Statistics Exam 3 Questions And Answers
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A binomial experiment is a random experiment consisting of n repeated trails such that -
ANS 1. the trails are independent
2. Each trial results in only 2 possible outcomes
3. the probability of success on each trail, denoted as p, is constant
For any value of p, as we [BLANK] the number of trails, n, the shape of the associated binomial
distribution approaches a normal distribution. - ANS increase
For a given Poisson process - ANS - The number of events over an interval is a discrete
random variable that follows a Poisson distribution
- The length of the interval between events is a continuous random variable that follows an
exponential distribution
A Poisson process is a model for a series of discrete events where: - ANS - The average time
between events is known but the exact timing of the evens is random
- The arrival of an event is independent of the event before
A Poisson process satisfies the following criteria: - ANS - Two events cannot occur at the
same point
- The average rate is constant
- The events are independent of each other
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The exponential distribution with parameter λ is the same as: - ANS - The Weibull
distribution with β=1 and δ=1/λ
- The gamma distribution with r=1 and λ =λ
For the student's t-distribution, and the Degrees of Freedom increase, the distribution
approaches a standard [BLANK] distributions - ANS Normal
If X is a binomial random variable and both np and n(1−p) are both greater than [BLANK], then
we can approximate the distribution of X using a Normal distribution with a Mean of np and a
standard deviation of root np(1−p). - ANS 5
a set of n independent random variables with the same distribution are called a -
ANS random sample
The probability distribution of a statistic sample is called its - ANS sampling distribution
The mean or expected value of the sample mean X is equal to the population mean μ and the
standard deviation of the sample mean, X, is equal to σ divided by - ANS the square root of
the sample size
If the population is normal then the distribution of the sample mean will be - ANS exactly
normal
if the population is NOT normal but the sample size is large enough then the distribution of the
sample mean will be - ANS Approximately normal
Sampling distributions allow us to preform statistical inference using these results we can -
ANS - Estimate unknown parameters with a specified confidence level
- Conduct hypothesis tests about parameters to compare them to target values