&14 Exam Questions And Correct
Answers.
Sampling from a finite population: - Answer choosing a simple
random sample of size n from a finite population of
size N where each possible sample of size n has the
same probability of being selected.
1. Assign a random number to each element of the
population.
2. Select the n elements corresponding to the n
smallest random numbers.
Sampling from an infinite population: - Answer A random sample of size n form an infinite
population is a sample selected meeting the
following:
1. Each element comes from the same population;
2. Each element is selected independently.
How would we take a finite random sample in excel? - Answer Use the formula =rand() which
will assign a random number between 0 and 1 for each data point, then copy and paste the
random number column (this is to ensure that the random numbers don't change when you
move it around- paste as values, not a formula) then sort the data by smallest to largest of the
random number values then select the smallest numbers- for example, if the sample is 30- from
the top down select thirty data points.
When you sort the data make sure if your data has headers to check the box indicating this
before you sort.
Point Estimation - Answer calculate the mean, standard deviation and
proportion. The resulting values are point estimators
for each.
, Variable used to describe proportion - Answer P for population proportion
p(bar)- for sample proportion
Sampling distribution - Answer a distribution of statistics obtained by selecting all the possible
samples of a specific size from a population
The Sampling Distribution of ̅ 𝑥 is the probability
distribution of all possible values of the sample mean
( ̅ 𝑥).
Standard Deviation of ̅ 𝑥 is denoted as 𝜎 ̅𝑥; and its
value depends on whether the population is finite or
infinite.
What is the standard error of the
mean - Answer the standard deviation of xbar- or the mean
Form/Shape of the Sampling Distribution of xbar - Answer The form/shape of the sampling
distribution of ̅ 𝑥 has
two possibilities:
1. The population has a normal distribution;
2. The population does not have a normal
distribution.
When the population has a normal distribution, the
sampling distribution of ̅ 𝑥 is normally distributed for
any sample size.
When the population does not have a normal
distribution the central limit theorem helps identify
the shape of the sampling distribution of ̅ 𝑥.