STT 231: Exam 2 Questions and Answers | 2025
Update | 100% Correct-Galen College of Nursing.
what two conditions must be met in order for the CLT to apply for proportional testing?
1: sample must be independent and identically distributed; like random assignment/sampling
2: sample must be sufficiently large
normal density curve
symmetric about the mean μ; has standard deviation σ; total area under the curve = 1.0; values of
the random variable X on x-axis; probabilities are represented by areas under the curve;
what do the numbers in the N(0,1) equation represent?
the first number is the mean (mu), and the second is the SD (sigma)
what is the equation for a standard normal curve/distribution, and what do the axes
represent?
N(0,1) the x axis represents z-scores, and the y is the probability
what is the domain for a standard normal curve? which particular interval are we
interested in?
the actual domain=infinite, but we are interested mostly in (mu +/- 3sigma)
difference between pnorm and qnorm commands
pnorm: gives proportion/percent of data within the given range
qnorm: gives the cutoff range for the percentile of data inputted
what are the required arguments for pnorm? qnorm?
, pnorm(upper cutoff, mean, SD)
qnorm(upper percentile cutoff, mean, SD)
when do you use the lower.tail=false argument?
during p/qnorm commands, when you are interested in the right side distribution
normal model for sampling distribution of pi hat
still follows the rule of standard normal curve (N(0,1)), but it uses N(pi, SE equation) because
what is standard error? how do you interpret the results?
it measures how close the current sample data reflects the overall population predicted data, a
high standard error value represents that your sample is not very reflective of the population and
is very spread out, vice versa for low
how do SE and sample size n relate?
as n increases, SE decreases, inverse relationship.
normal model for a sampling distribution of x bar
still N(0,1) template, but the mean is represented by mu, and the SD is sigma/square root of n
when should you use normal model sampling distribution of x bar and when for pi hat?
x bar if you are given mu in the problem, pi hat if you are given pi in the problem
if you are given a problem that gives the sample mean and asks for the proportion greater
than or equal to a z score, what would you do?
Update | 100% Correct-Galen College of Nursing.
what two conditions must be met in order for the CLT to apply for proportional testing?
1: sample must be independent and identically distributed; like random assignment/sampling
2: sample must be sufficiently large
normal density curve
symmetric about the mean μ; has standard deviation σ; total area under the curve = 1.0; values of
the random variable X on x-axis; probabilities are represented by areas under the curve;
what do the numbers in the N(0,1) equation represent?
the first number is the mean (mu), and the second is the SD (sigma)
what is the equation for a standard normal curve/distribution, and what do the axes
represent?
N(0,1) the x axis represents z-scores, and the y is the probability
what is the domain for a standard normal curve? which particular interval are we
interested in?
the actual domain=infinite, but we are interested mostly in (mu +/- 3sigma)
difference between pnorm and qnorm commands
pnorm: gives proportion/percent of data within the given range
qnorm: gives the cutoff range for the percentile of data inputted
what are the required arguments for pnorm? qnorm?
, pnorm(upper cutoff, mean, SD)
qnorm(upper percentile cutoff, mean, SD)
when do you use the lower.tail=false argument?
during p/qnorm commands, when you are interested in the right side distribution
normal model for sampling distribution of pi hat
still follows the rule of standard normal curve (N(0,1)), but it uses N(pi, SE equation) because
what is standard error? how do you interpret the results?
it measures how close the current sample data reflects the overall population predicted data, a
high standard error value represents that your sample is not very reflective of the population and
is very spread out, vice versa for low
how do SE and sample size n relate?
as n increases, SE decreases, inverse relationship.
normal model for a sampling distribution of x bar
still N(0,1) template, but the mean is represented by mu, and the SD is sigma/square root of n
when should you use normal model sampling distribution of x bar and when for pi hat?
x bar if you are given mu in the problem, pi hat if you are given pi in the problem
if you are given a problem that gives the sample mean and asks for the proportion greater
than or equal to a z score, what would you do?
