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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods |
| Topic 2: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
| Topic 3: Foundations of Data-Driven Decision Making | 20% | - Role of data in business decisions - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance |
| Topic 4: Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance |
| Topic 5: Probability & Statistical Inference | 20% | - Correlation vs. causation - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value |
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NEW QUESTION # 23
Why is choosing appropriate performance indicators important for data-driven decision-making?
Answer: A
Explanation:
Performance indicators guide behavior, priorities, and decision-making. In data-driven decision making, selecting theright indicators ensures alignment with an organization's definition of success. Metrics determine what is measured, monitored, and improved.
If indicators are poorly chosen, organizations may optimize the wrong outcomes, leading to unintended consequences. Well-aligned indicators translate strategic goals into measurable targets and ensure that analytics support meaningful results.
Rules, motivation, and compliance may be secondary effects, but the primary purpose of performance indicators is strategic alignment. Therefore, the correct answer isC.
NEW QUESTION # 24
A nonprofit organization ran a fundraiser and would like to determine the amount of a typical donation.
Which statistic is less affected by outliers and skewed data and should be used to determine the amount of a typical donation?
Answer: A
Explanation:
In data-driven decision making, themedianis the preferred measure of central tendency when data contain outliers or are skewed. Fundraising donation amounts often exhibit right-skewed distributions, where a small number of very large donations can significantly inflate the mean. Using the mean in such cases may misrepresent what a "typical" donor gives.
The median represents the middle value when donation amounts are ordered from smallest to largest. Because it depends only on position rather than magnitude, it isrobust to extreme values. This makes it especially useful for summarizing typical behavior in skewed financial data.
The mean is sensitive to outliers, the z-score measures standardized distance from the mean, and the mode identifies the most frequent value but may not reflect central tendency in continuous donation data. Therefore, the statistic that best represents a typical donation amount is themedian, making optionCcorrect.
NEW QUESTION # 25
Why are sample sizes important for ensuring statistical significance?
Answer: A
Explanation:
Sample size is critical for ensuring **statistical significance** because it determines whether results can be confidently generalized to a larger population. In data-driven decision making, larger and appropriately selected samples reduce sampling error and increase the reliability of statistical estimates.
When sample sizes are too small, observed effects may be due to random variation rather than true underlying patterns. Larger samples provide more precise estimates of population parameters and increase the power of hypothesis tests, making it easier to detect meaningful differences or relationships.
While increasing sample size does not eliminate researcher bias, prevent hypothesis misinterpretation, or remove the need for further analysis, it strengthens the validity of conclusions. Statistical significance depends on sample size, effect size, and variability, all of which influence confidence in results.
Therefore, the correct answer is **A**, as adequate sample sizes allow accurate conclusions to be confidently applied to larger populations.
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NEW QUESTION # 26
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: B,C
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 # 27
How should a marketing consulting firm perform a cluster analysis for a new granola bar?
Answer: D
Explanation:
Cluster analysisis an unsupervised learning technique used to group observations based on similarity. In data- driven decision making, it is commonly used formarket segmentation, allowing firms to identify distinct customer groups with similar preferences or behaviors.
For a new granola bar, cluster analysis helps determine which consumer segments exist, such as health- conscious buyers, convenience-focused consumers, or price-sensitive shoppers. This enables targeted marketing strategies and product positioning.
Understanding reasons for purchase requires survey or causal analysis, not clustering. Competitor benchmarking and trend analysis involve different analytical techniques.
Therefore, the correct answer isB, determining different segments or groups to target.
NEW QUESTION # 28
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