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PORTAGE STATISTICS CNSL 503 MODULE 3
STATS TEST EXAM|| QUESTIONS AND 100%
CORRECT ANSWERS ALREADY GRADED A+||
LATEST AND COMPLETE UPDATE WITH
VERIFIED SOLUTIONS GUARANTEED PASS!!!
What is the difference between a parameter and a statistic? - ANSWER: A
parameter is a numerical value that describes a population, such as the population
mean. A statistic is a numerical value that describes a sample, such as a sample
mean
A statistic is a descriptive statistical result that is generated from a sample, whereas
a parameter is a statistical result from a population..
Why is it important to have a representative sample? - ANSWER: It is important
to have a representative sample, because we're trying to make inferences about a
certain population. In order to make correct inferences about this population, the
sample must reflect the population. For instance, not having a representative
sample is what called Literary Digest to predict Alf Landon to win, when FDR
actually won in a land slide.
Representative sample - ANSWER: consists of members that possess the same
characteristics as those of the population
(e.g. age distribution)
biased sample - ANSWER: a sample that is not representative of the population
simple random sampling - ANSWER: -every member of the population has an
equal chance of being selected for the sample
-the probability that each member is selected is independent of one another
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-computers are often used to generate random numbers
-best for small sample sizes
systematic sampling - ANSWER: random sampling with a system in which the
starting point is random and each subsequent member selected is based on a fixed
interval (e.g. every 3rd person)
- the probability that each member is selected is not independent of one another
-random samples can be achieved in a way that is more efficient than simple
random samlping
stratified sampling - ANSWER: is when you divide the population into strata
(typically based on age, gender, race, socioeconomic status) and then you
randomly select people to sample from each strata
(not everyone in a strata is studied)
-great for populations with subsections/ different characterstics
cluster sampling - ANSWER: is when you divide the population into clusters such
as the U.S. voters into voters by states, and then you randomly select clusters (e.g.
CA, NY, KY). From there, everyone in that cluster is studied.
-great for very large populations
convenience sampling - ANSWER: selecting a sample that is convenient and easy
to access
- asking everyone in a lecture hall to determine L handedness at your school
-does NOT result in a representative sample
PORTAGE STATISTICS CNSL 503 MODULE 3
STATS TEST EXAM|| QUESTIONS AND 100%
CORRECT ANSWERS ALREADY GRADED A+||
LATEST AND COMPLETE UPDATE WITH
VERIFIED SOLUTIONS GUARANTEED PASS!!!
What is the difference between a parameter and a statistic? - ANSWER: A
parameter is a numerical value that describes a population, such as the population
mean. A statistic is a numerical value that describes a sample, such as a sample
mean
A statistic is a descriptive statistical result that is generated from a sample, whereas
a parameter is a statistical result from a population..
Why is it important to have a representative sample? - ANSWER: It is important
to have a representative sample, because we're trying to make inferences about a
certain population. In order to make correct inferences about this population, the
sample must reflect the population. For instance, not having a representative
sample is what called Literary Digest to predict Alf Landon to win, when FDR
actually won in a land slide.
Representative sample - ANSWER: consists of members that possess the same
characteristics as those of the population
(e.g. age distribution)
biased sample - ANSWER: a sample that is not representative of the population
simple random sampling - ANSWER: -every member of the population has an
equal chance of being selected for the sample
-the probability that each member is selected is independent of one another
, 2|Page
-computers are often used to generate random numbers
-best for small sample sizes
systematic sampling - ANSWER: random sampling with a system in which the
starting point is random and each subsequent member selected is based on a fixed
interval (e.g. every 3rd person)
- the probability that each member is selected is not independent of one another
-random samples can be achieved in a way that is more efficient than simple
random samlping
stratified sampling - ANSWER: is when you divide the population into strata
(typically based on age, gender, race, socioeconomic status) and then you
randomly select people to sample from each strata
(not everyone in a strata is studied)
-great for populations with subsections/ different characterstics
cluster sampling - ANSWER: is when you divide the population into clusters such
as the U.S. voters into voters by states, and then you randomly select clusters (e.g.
CA, NY, KY). From there, everyone in that cluster is studied.
-great for very large populations
convenience sampling - ANSWER: selecting a sample that is convenient and easy
to access
- asking everyone in a lecture hall to determine L handedness at your school
-does NOT result in a representative sample
