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The Qualified Medicare Beneficiary (QMB) 3200 Bliss Exam 2 has 100% correct answers for all questions. $9.00
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The Qualified Medicare Beneficiary (QMB) 3200 Bliss Exam 2 has 100% correct answers for all questions.
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Qualified Medicare Beneficiary (QMB) 3200 Bliss Exam 2 with All Questions and 100% Correct Answers

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  • October 21, 2024
  • 70
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers

Subjects

  • qualified medicare beneficiary qmb 3200 bliss e
  • the sample proportion is the point estimator of
  • statistics involve descriptive statistics which i

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  • Qualified Medicare Beneficiary 3200
  • Qualified Medicare Beneficiary 3200

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73 Multiple choice questions

Term 1 of 73
The sample proportion , is the point estimator of the:

population proportion, p.

population of the USA

the population is first divided up into mutually exclusive and collectively exhaustive
groups, called clusters. a cluster sample includes observations from randomly selected
clusters.

the tendency of a sample statistic to systematically over or under estimate a population
parameter.

Term 2 of 73
Statistics involve: Descriptive statistics which is?

describing the data, usually in a tabular or graphical format.

ignoring the data and making assumptions.

conducting experiments to gather data.


making predictions about the future.

Term 3 of 73
True or False: The standard error of x ̅ and p ̅ DECREASES as we INCREASE n

a normal distribution whenever the sample size is 30 or more


True, because when you increase the sample size you decrease variability.

the distribution of the x ̅ and p ̅ becomes approximately normal.


Remove as much Bias as possible to get a good sample

,Term 4 of 73
A Population?

the average of a set of numbers.


consists of all items of interest in a statistical problem.

a group of people living in a specific area.


the result of a mathematical equation.

Term 5 of 73
A sample(statistic) is used to:

determine the sample size needed for a study.

calculate the mean of a dataset.

make inferences about a unknown population parameter.


identify outliers in a dataset.

Term 6 of 73
Symbols: u

sep ̅=σp ̅= sqrt p(1-p)/n

is the mean for the population


e(x ̅)= mean

if you know what the whole pizza looks like, then i can tell you how likely certain pieces
are to occur.

Term 7 of 73
With sampling distribution, as n (sample size) increases:

estimator


e(x ̅)= mean

True, because when you increase the sample size you decrease variability.

the distribution of the x ̅ and p ̅ becomes approximately normal.

,Term 8 of 73
Sample distribution Properties: Sample Proportion formula

Up ̅=p(true proportion)


E(x ̅)= mean

True

Σx ̅

Term 9 of 73
Symbols: s

ux ̅=u


sep ̅=σp ̅= sqrt p(1-p)/n

if you know what the whole pizza looks like, then i can tell you how likely certain pieces
are to occur.

is the sample standard deviation

Term 10 of 73
The mean is also known as:

Standard Deviation


Expected Value

Variance

Standard Error Of The Mean

, Term 11 of 73
Point Estimation is?

the population is first divided up into mutually exclusive and collectively exhaustive
groups, called clusters. A cluster sample includes observations from randomly selected
clusters.

the procedure to determine the sample mean, standard deviation and proportion is
known as point estimation. The values that are determined are known as the point
estimate.

you would get a normal bell curve with the most frequent observations being near the
mean of the population and the proportion of the population.

statistical inference

Term 12 of 73
Examples of a finite population:

the population is first divided up into mutually exclusive and collectively exhaustive
groups, called clusters. A cluster sample includes observations from randomly selected
clusters.

Estimator

Sep ̅=σp ̅= sqrt p(1-p)/n

They are often defined by list such as: Organization membership roster, Credit card
account numbers, and Inventory product numbers.

Term 13 of 73
The sampling distribution of x ̅has an:

sep ̅=σp ̅= sqrt p(1-p)/n

Expected Value

expected value or mean, a standard deviation, and expected shape.


estimator

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