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| Section | Objectives |
|---|---|
| Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
| Hypothesis Testing | - Null and alternative hypotheses - t-tests, chi-square tests, and significance testing |
| Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
| Data Fundamentals and Business Analytics | - Data types and data collection methods - Descriptive statistics (mean, median, variance, standard deviation) |
| Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
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NEW QUESTION # 19
Research data indicate 95% confidence in a study in which subjects who were shown a product advertisement exhibited brand awareness compared to a control group who did not see the advertisement.
What can be concluded from this study?
Answer: B
Explanation:
A 95% confidence result indicates a statistically significant difference between groups. Since the measured outcome isbrand awareness, the correct conclusion is that the advertisement was effective in increasing brand awareness.
Confidence levels do not measure sales, preference, or dislike. Therefore, the correct answer isB.
NEW QUESTION # 20
Which process is designed to proactively prevent a problem?
Answer: A
Explanation:
Quality assurance is the process designed to proactively prevent problems before they occur. It focuses on improving the systems, procedures, and standards used to produce outcomes so that defects, errors, or failures are less likely to happen in the first place. This preventive orientation distinguishes quality assurance from quality control, which is more concerned with identifying defects after or during production through inspection and monitoring. Common cause variation refers to the natural variability present in a stable process and is not itself a preventive process. The plan-do-check-act cycle is a structured improvement framework, but it is broader and not the specific term used to describe proactive prevention. In data-driven decision- making and quality management, assurance activities often include standardizing procedures, training employees, documenting workflows, and building reliable systems that reduce variation and improve consistency. Because the question asks which process is specifically designed to act proactively, the best answer is quality assurance. It aims to stop issues before they occur, making it a preventive rather than reactive approach.
NEW QUESTION # 21
What is the purpose of the quality management principle of dedication to fact-based decision-making?
Answer: B
Explanation:
The principle offact-based decision-makingemphasizes using reliable data and objective analysis rather than intuition or opinion. In data-driven decision making, this principle exists primarily toreduce bias and increase trust in organizational plans and decisions.
When decisions are grounded in verified data, assumptions are challenged, personal biases are minimized, and outcomes are more predictable. This builds confidence among stakeholders and supports transparency and accountability.
Customer loyalty, waste elimination, and quality effectiveness may be indirect benefits, but the core purpose is ensuring that decisions are objective, defensible, and evidence-based. Therefore, the correct answer isD.
NEW QUESTION # 22
How should a marketing consulting firm perform a cluster analysis for a new granola bar?
Answer: C
NEW QUESTION # 23
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: C,D
Explanation:
Good data quality management plays a critical role in improving business decision-making by ensuring that data is accurate, complete, and reliable. One key benefit is that itensures there are no missing data points, which helps maintain data completeness. Missing data can distort results, reduce analytical power, and lead to incorrect conclusions, especially in descriptive and inferential statistics.
Another important benefit is that data quality managementmitigates undetected errors from the data-entry process. Errors such as duplicate entries, incorrect values, or inconsistent formats can significantly bias analysis if left unnoticed. Through validation checks, cleaning procedures, and governance standards, organizations reduce the risk of flawed insights.
While good data quality supports better analysis, it does not guarantee statistical significance, as significance depends on sample size, variability, and study design. Similarly, it does not necessarily make the statistical process faster; in fact, data cleaning can be time-consuming. However, it improves theaccuracy and trustworthinessof outcomes.
In data-driven decision making, high-quality data is essential because decisions are only as good as the data used to support them. Therefore, the correct answers areA and D.
NEW QUESTION # 24
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