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| Section | Objectives |
|---|---|
| Topic 1: Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
| Topic 2: Decision Making Models | - Decision trees and expected value analysis - Risk and uncertainty in decision-making |
| Topic 3: Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Topic 4: Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
| Topic 5: Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
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NEW QUESTION # 68
Which two statements describe Ishikawa's seven basic tools of quality?
Choose 2 answers.
Answer: B,C
Explanation:
Ishikawa'sseven basic tools of qualitywere designed to be simple, visual, and accessible. In data-driven decision making, these tools help employees identify, analyze, and solve quality problems without requiring advanced statistical expertise.
The tools-such as flowcharts, histograms, Pareto charts, and cause-and-effect diagrams-represent processes graphically, making patterns and issues easier to understand. Additionally, they are intentionally designed so thatan average worker can easily understand and use them, supporting organization-wide quality improvement.
They do not rely on photographic representations, nor are they intended for advanced or expert-level training.
Instead, they empower frontline employees to participate in continuous improvement efforts.
Therefore, the correct answers areA and C.
NEW QUESTION # 69
Which type of analytics classification uses experimental design and optimization to suggest a course of action?
Answer: C
Explanation:
Prescriptive analyticsis the analytics classification that uses experimental design and optimization techniques to suggest a specific course of action. In data-driven decision making, prescriptive analytics represents the most advanced stage of analytics, as it not only predicts outcomes but also recommends decisions that lead to optimal results.
Descriptive analytics summarizes historical data to explain what has already happened, while predictive analytics uses statistical and probabilistic models to estimate what is likely to happen in the future. Diagnostic analytics focuses on understanding why something happened by identifying root causes. In contrast, prescriptive analytics answers the critical question:what should be done.
Prescriptive analytics relies on methods such as optimization models, simulation, decision trees, and experimental design. These techniques evaluate multiple scenarios, constraints, and objectives to identify the best possible action. For example, organizations use prescriptive analytics to optimize pricing, allocate resources efficiently, schedule operations, or determine optimal investment strategies.
Within data-driven decision-making frameworks, prescriptive analytics bridges analysis and action by directly supporting managerial decision-making. It transforms analytical insights into concrete recommendations that can be implemented to improve performance and outcomes. Therefore, the correct answer isC, as prescriptive analytics explicitly uses experimental design and optimization to suggest a course of action.
NEW QUESTION # 70
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: B
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 # 71
For which situation could a scatter diagram be used?
Answer: B
Explanation:
Ascatter diagramis used to visually examine therelationship between two quantitative variables. In data- driven decision making, scatter diagrams help analysts assess whether variables move together, whether the relationship is positive, negative, or nonexistent, and whether the relationship appears linear or nonlinear.
Each point on a scatter diagram represents a paired observation of two variables, such as advertising spend and sales revenue or hours studied and test scores. Patterns in the plotted points can suggest correlation, which may later be explored using regression analysis. Scatter diagrams are exploratory tools and do not, by themselves, establish causation.
A prioritization matrix ranks options, frequency differences are examined using bar or Pareto charts, and differences in means are evaluated using hypothesis tests such as t-tests or ANOVA. Therefore, the correct application of a scatter diagram is to demonstraterelationships between variables, making optionBcorrect.
NEW QUESTION # 72
Which process is designed to proactively prevent a problem?
Answer: D
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 # 73
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