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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation - Probability rules, distributions, expected value |
| Topic 2: Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions |
| Topic 3: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
| Topic 4: Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance |
| Topic 5: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods |
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NEW QUESTION # 26
Which statement is an accurate reflection of an analytical decision made by a for-profit organization?
Answer: B
Explanation:
A for-profit organization typically uses analytical methods to improve outcomes such as revenue, efficiency, market share, and especially profit. Among the available choices, linear programming is the recognized analytical optimization technique used to maximize or minimize an objective function subject to constraints.
In business settings, firms may use linear programming to optimize product mix, staffing, shipping routes, inventory allocation, advertising budgets, or production schedules. The purpose is to find the best possible decision based on limited resources such as time, labor, capital, or materials. The other options do not reflect standard analytical decision-making terminology in data-driven management. "Software programming" and
"hardware programming" refer to technology development activities, not profit-optimization methods, and
"social programming" does not describe a formal business optimization tool. Because the question asks for an accurate reflection of an analytical decision in a for-profit context, the correct answer is the one that uses a recognized prescriptive analytics method to optimize profit. That method is linear programming, which is widely used in operations research and managerial decision-making.
NEW QUESTION # 27
Two project teams are assigned to upgrade an on-premise data warehouse to a cloud-based data lake in 13 months. The infrastructure team has five team members, while the enterprise analytics team has three team members. The enterprise analytics team cannot move into production until the infrastructure team has completed the migration.
What should be used to find the probability that the project will be completed on time?
Answer: A
Explanation:
This scenario requires the use of **conditional probability**, which applies when the likelihood of one event depends on the occurrence of another event. In data-driven decision making, conditional probability is used to model dependent events within processes, workflows, and project timelines.
In this case, the enterprise analytics team's ability to move into production is **dependent on** the infrastructure team completing the migration. Because one event cannot occur unless another event has already occurred, the probability of completing the project on time must account for this dependency.
The multiplication principle applies to independent events, Bayes' theorem updates probabilities based on new information, and combinations are used for counting outcomes, not dependency analysis. Conditional probability explicitly captures the relationship between dependent tasks.
Project risk analysis and scheduling often rely on conditional probability to assess completion likelihood when tasks are sequentially linked. Therefore, the correct answer is **C**, conditional probability.
NEW QUESTION # 28
A manager has been assigned to manage a digital marketing analytics team. The manager tasks the team with determining similarities among existing customers in the company's database, such as similarities in products purchased, location, and the average amount spent per order among existing customers.
Which type of activity will help the team accomplish this task?
Answer: A
Explanation:
Data miningis the appropriate activity for identifying patterns, similarities, and relationships within large datasets. In data-driven decision making, data mining techniques such as clustering and association analysis are commonly used to segment customers based on behavior and characteristics.
The task described involves uncovering hidden patterns across multiple variables, which aligns directly with data mining objectives. Linear programming focuses on optimization, regression predicts outcomes, and touchpoint analysis examines customer interactions rather than similarities.
Therefore, the correct answer isA, data mining.
NEW QUESTION # 29
The U.S. Postal Service wants to know if local first-class mail is being delivered within two days of postmark.
Which key performance indicator (KPI) should the Postal Service use?
Answer: A
Explanation:
On-time performanceis the most appropriate KPI for measuring whether mail is delivered within a specified timeframe. In data-driven decision making, KPIs must align directly with operational objectives.
The Postal Service's goal is to assess delivery timeliness. On-time performance measures the percentage of mail delivered within the expected service standard, making it a direct and objective indicator.
Customer satisfaction and employee morale provide valuable insights but do not directly measure delivery speed. Incentive performance rate is unrelated to delivery outcomes.
Therefore, the correct answer isC, on-time performance.
NEW QUESTION # 30
A spa wants to determine which duration of its massage packages is the most popular. The spa has 30-minute,
50-minute, and 75-minute sessions. Which statistical measure should be used and is less affected by outliers and skewed data?
Answer: B
Explanation:
The spa wants to know which package duration is the most popular, meaning which session length occurs most frequently in the data. The statistical measure used to identify the most frequently occurring category or value is the mode. Because the massage durations are offered in discrete categories of 30, 50, and 75 minutes, the mode is the best measure for determining customer preference. It directly identifies the package selected most often. Standard deviation measures variability, not popularity. Mean and median are measures of central tendency, but they do not answer the question of which option is chosen most frequently. Although the wording mentions being less affected by outliers and skewed data, the strongest reason to choose the mode is that popularity is fundamentally a frequency question. The mode is especially useful for categorical or discrete-choice data where the goal is to identify the most common option rather than the center of a numerical distribution. Therefore, the correct answer is mode because it shows which massage duration customers prefer most often.
NEW QUESTION # 31
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