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QMB 3200 Business Statistics Final
Exam | UCF | Definition-Based
Vocabulary Review | Verified Q&A
Exam Structure:
Subject: Business Statistics
Source: QMB 3200 UCF Final Exam Vocabulary Compilation
Format: Definition-Based Q&A
1. What is a parameter?
Correct Answer: A numerical characteristic of a population, such as a
population mean µ, a population standard deviation σ, population
proportion p, and so on.
Rationale:
1. Parameters describe entire populations, not samples.
2. They are fixed values, though often unknown in practice.
3. Common examples include the population mean (µ) and population
proportion (p).
4. Understanding parameters is essential for statistical inference and
hypothesis testing.
2. What is the target population?
Correct Answer: The population for which statistical inferences such as
point estimates are made. It is important for the target population to
correspond as closely as possible to the sampled population.
Rationale:
1. Target population is the group researchers want to draw conclusions
about.
2. Mismatch between target and sampled populations can cause bias.
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3. Clear definition of the target population ensures valid inference.
4. It guides sampling design and data collection.
3. What is the sampled population?
Correct Answer: The population from which the sample is taken.
Rationale:
1. Sampled population is the actual accessible group for sampling.
2. Ideally, it should perfectly match the target population.
3. Differences between sampled and target populations threaten external
validity.
4. Practical constraints often define the sampled population.
4. What is a sampling distribution?
Correct Answer: A probability distribution consisting of all possible values
of a sample statistic.
Rationale:
1. Sampling distribution describes variability of a statistic across all possible
samples.
2. It forms the basis for confidence intervals and hypothesis tests.
3. The central limit theorem describes its shape for sample means.
4. Its standard deviation is the standard error.
5. What is a sample statistic?
Correct Answer: A sample characteristic, such as a sample mean x̄ , a
sample standard deviation s, a sample proportion p̄ , and so on. The value of
the sample statistic is used to estimate the value of the corresponding
population parameter.
Rationale:
1. Sample statistics are computed from observed data.
2. They serve as point estimators of population parameters.
3. Common examples include x̄ (mean), s (SD), and p̄ (proportion).
4. Their accuracy depends on sampling method and sample size.
6. What is the finite population correction factor?
Correct Answer: The term √((N-n)/(N-1)) that is used in the formulas for
standard deviation of x̄ and p̄ whenever a finite population, rather than an
QMB 3200 Business Statistics Final
Exam | UCF | Definition-Based
Vocabulary Review | Verified Q&A
Exam Structure:
Subject: Business Statistics
Source: QMB 3200 UCF Final Exam Vocabulary Compilation
Format: Definition-Based Q&A
1. What is a parameter?
Correct Answer: A numerical characteristic of a population, such as a
population mean µ, a population standard deviation σ, population
proportion p, and so on.
Rationale:
1. Parameters describe entire populations, not samples.
2. They are fixed values, though often unknown in practice.
3. Common examples include the population mean (µ) and population
proportion (p).
4. Understanding parameters is essential for statistical inference and
hypothesis testing.
2. What is the target population?
Correct Answer: The population for which statistical inferences such as
point estimates are made. It is important for the target population to
correspond as closely as possible to the sampled population.
Rationale:
1. Target population is the group researchers want to draw conclusions
about.
2. Mismatch between target and sampled populations can cause bias.
, 2|Page
3. Clear definition of the target population ensures valid inference.
4. It guides sampling design and data collection.
3. What is the sampled population?
Correct Answer: The population from which the sample is taken.
Rationale:
1. Sampled population is the actual accessible group for sampling.
2. Ideally, it should perfectly match the target population.
3. Differences between sampled and target populations threaten external
validity.
4. Practical constraints often define the sampled population.
4. What is a sampling distribution?
Correct Answer: A probability distribution consisting of all possible values
of a sample statistic.
Rationale:
1. Sampling distribution describes variability of a statistic across all possible
samples.
2. It forms the basis for confidence intervals and hypothesis tests.
3. The central limit theorem describes its shape for sample means.
4. Its standard deviation is the standard error.
5. What is a sample statistic?
Correct Answer: A sample characteristic, such as a sample mean x̄ , a
sample standard deviation s, a sample proportion p̄ , and so on. The value of
the sample statistic is used to estimate the value of the corresponding
population parameter.
Rationale:
1. Sample statistics are computed from observed data.
2. They serve as point estimators of population parameters.
3. Common examples include x̄ (mean), s (SD), and p̄ (proportion).
4. Their accuracy depends on sampling method and sample size.
6. What is the finite population correction factor?
Correct Answer: The term √((N-n)/(N-1)) that is used in the formulas for
standard deviation of x̄ and p̄ whenever a finite population, rather than an
