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| Section | Objectives |
|---|---|
| Topic 1: Data-Driven Culture and Communication | - Ethical Considerations in Data Use - Building Data-Informed Organizations - Stakeholder Communication - Presenting Data Insights |
| Topic 2: Decision-Making Frameworks | - Cost-Benefit Analysis - Evidence-Based Decision Making - Rational Decision Models - Risk Analysis and Assessment |
| Topic 3: Business Intelligence and Analytics | - Dashboards and Reporting - Key Performance Indicators (KPIs) - Data Mining Concepts - Predictive Analytics Basics |
| Topic 4: Data Analysis Fundamentals | - Data Types and Measurement Scales - Descriptive Statistics - Data Quality and Cleaning - Data Visualization Techniques |
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NEW QUESTION # 60
Which two statements describe Ishikawa's seven basic tools of quality?
Choose 2 answers.
Answer: C,D
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 # 61
A sample of 1,020 people was asked how many minutes they exercise on a typical day. The data were plotted and determined to beskewed leftwith a mean of 44.75 minutes.
Which boxplot correctly graphs this data?
Answer: D
NEW QUESTION # 62
An organization develops a new strategic plan and seeks ways to improve process performance by reducing variation to only 3.4 defects per million process outputs.
Which tool can the organization use to meet this goal?
Answer: D
Explanation:
Six Sigmais specifically designed to reduce process variation to3.4 defects per million opportunities, making it the correct tool for achieving this goal. In data-driven decision making, Six Sigma provides a structured methodology (DMAIC) for defining problems, measuring performance, analyzing root causes, improving processes, and controlling future outcomes.
Statistical process control supports monitoring, but Six Sigma integrates SPC with strategic improvement initiatives. Linear programming optimizes resource allocation, and just-in-time focuses on inventory efficiency rather than defect reduction.
Therefore, the correct answer isD, Six Sigma.
NEW QUESTION # 63
Why are sample sizes important for ensuring statistical significance?
Answer: B
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 # 64
Which type of study is also known as a quasi-experimental study?
Answer: A
Explanation:
A **quasi-experimental study** is commonly referred to as an **observational study** in data-driven decision making. Unlike true experiments, quasi-experimental studies do not involve random assignment of subjects to treatment and control groups. Instead, researchers observe outcomes in naturally occurring groups and attempt to draw conclusions about relationships between variables.
In observational studies, the researcher does not control the assignment of treatments. As a result, these studies are more susceptible to bias and confounding variables than randomized experiments. However, they are often necessary when controlled experimentation is impractical, unethical, or too costly. For example, studying the impact of policy changes or economic conditions typically relies on observational data.
Blind studies are a form of experimental design used to reduce bias, hypothesis testing is a statistical process rather than a study type, and content validity refers to measurement quality. None of these represent quasi- experimental designs.
In data-driven decision making, observational (quasi-experimental) studies are valuable for identifying associations and generating insights, but analysts must be cautious not to infer causality without proper controls. Therefore, the correct answer is **A**.
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NEW QUESTION # 65
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