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| Section | Weight | Objectives |
|---|---|---|
| Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value |
| Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions |
| Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards |
| Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance |
| Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value - Correlation vs. causation |
1. Why is quantitative analysis important to the decision-making process?
A) It examines and describes large sets of data.
B) It increases the experience of top management.
C) It creates a risk-management dashboard.
D) It provides definable metric-analysis surveys.
2. Why is choosing appropriate performance indicators important for data-driven decision-making?
A) They convince employees that they are equally as important as their managers.
B) They align with an organization's definition of success.
C) They are a key part of an organization's rules and bylaws.
D) They satisfy governmental regulations.
3. Two vendors sell a dozen raw materials that a company needs to build its products. Which type of analysis should be used to determine if one of the vendors has a better average price at a given point in time?
A) Graphical analysis
B) Heuristic analysis
C) Background analysis
D) Data analysis
4. Why are sample sizes important for ensuring statistical significance?
A) So that no additional analysis is required
B) So that the possibility of researcher bias is eliminated
C) So that accurate conclusions can be confidently applied to larger populations
D) So that a hypothesis cannot be misinterpreted
5. How should a marketing consulting firm perform a cluster analysis for a new granola bar?
A) Determine the reasons for consumer granola bar purchases
B) Determine whether there are any granola bar sales trends
C) Determine the different segments or groups to target
D) Determine competitor benchmarks and forecasts for comparison
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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