Business Statistics Final Exam | Latest Update | 2024/2025 | 100% Pass
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Course
Business Statistics
Institution
Business Statistics
Business Statistics Final Exam | Latest
Update | 2024/2025 | 100% Pass
How can the Central Limit Theorem be applied to improve decision-making in business
statistics?
The Central Limit Theorem helps in approximating the distribution of sample means to a
normal distribution, allowing busin...
Business Statistics Final Exam | Latest
Update | 2024/2025 | 100% Pass
How can the Central Limit Theorem be applied to improve decision-making in business
statistics?
✔✔ The Central Limit Theorem helps in approximating the distribution of sample means to a
normal distribution, allowing businesses to make inferences about population parameters even
with smaller sample sizes.
What is the significance of the p-value in hypothesis testing, and how does it impact decision-
making in a business context?
✔✔ The p-value indicates the probability of observing the test results under the null hypothesis.
A low p-value suggests strong evidence against the null hypothesis, guiding business decisions
regarding the validity of the tested hypothesis.
Explain the difference between correlation and causation, and provide an example of how each
might be used in business analysis.
✔✔ Correlation measures the strength and direction of a relationship between two variables,
while causation implies that one variable directly affects another. For example, a correlation
might show that increased advertising spending is associated with higher sales, but causation
would require proving that the advertising directly caused the increase in sales.
1
, How is a confidence interval used to estimate a population parameter in business statistics, and
what does it convey about the reliability of the estimate?
✔✔ A confidence interval provides a range of values within which the true population parameter
is likely to fall with a specified level of confidence. It conveys the precision and reliability of the
estimate, with wider intervals indicating less precision.
What are the assumptions underlying linear regression analysis, and why is it important to check
these assumptions in business modeling?
✔✔ Key assumptions of linear regression include linearity, independence of errors,
homoscedasticity (constant variance of errors), and normality of error terms. Checking these
assumptions is crucial to ensure the validity and reliability of the regression model's results.
Describe the concept of statistical power and its importance in designing business experiments.
✔✔ Statistical power refers to the probability of correctly rejecting a false null hypothesis. High
power is important in business experiments to ensure that the study has a good chance of
detecting a true effect, if one exists, thereby avoiding Type II errors.
What role does variance play in understanding the distribution of data in business statistics?
2
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