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| Section | Objectives |
|---|---|
| Topic 1: Data-Driven Culture and Communication | - Building Data-Informed Organizations - Stakeholder Communication - Presenting Data Insights - Ethical Considerations in Data Use |
| Topic 2: Data Analysis Fundamentals | - Descriptive Statistics - Data Visualization Techniques - Data Quality and Cleaning - Data Types and Measurement Scales |
| Topic 3: Decision-Making Frameworks | - Rational Decision Models - Cost-Benefit Analysis - Risk Analysis and Assessment - Evidence-Based Decision Making |
| Topic 4: Business Intelligence and Analytics | - Key Performance Indicators (KPIs) - Dashboards and Reporting - Data Mining Concepts - Predictive Analytics Basics |
>> Data-Driven-Decision-Making Examsfragen <<
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15. Frage
Which tool sorts data into categories to help teams identify the most significant factors that contribute the most to problems?
Antwort: D
Begründung:
APareto chartsorts data into categories and ranks them by frequency or impact. In data-driven decision making, this helps teams focus on the most significant contributors to a problem.
The chart combines bars and a cumulative line to highlight which factors account for the largest share of issues. This aligns with the Pareto principle and supports prioritization of improvement efforts.
Run charts track data over time, flowcharts describe processes, and cause charts are not a standard quality tool. Therefore, the correct answer isC.
16. Frage
Why are experiments conducted using random sample populations?
Antwort: B
Begründung:
Experiments and studies commonly use random samples because observing an entire population is often too difficult, costly, time-consuming, or impractical. In most real-world settings, researchers cannot collect data from every individual, item, or event of interest. A properly selected random sample allows them to estimate population characteristics with a manageable amount of effort while still preserving the ability to make statistical inferences. Random sampling improves representativeness and reduces selection bias, but it does not guarantee total elimination of bias. That is why the option claiming bias elimination is incorrect.
Likewise, the issue is not mainly about outliers or software cost. The main reason for using random samples is feasibility combined with inferential validity. With sound sampling methods, researchers can use probability theory to generalize findings from the sample to the broader population and estimate the degree of uncertainty in those conclusions. Therefore, the strongest and most accurate answer is that studying the entire population is difficult and impractical, making random sampling the preferred and efficient alternative.
17. Frage
What is true about outliers?
Choose 2 answers.
Antwort: A,D
Begründung:
Outliers are observations that differ substantially from the rest of the data, but they are not automatically errors and should not be removed without investigation. One correct statement is that miskeyed outliers can be corrected before analysis. For example, if a value of 500 is entered instead of 50, this is a data-entry error, not a meaningful observation, and it should be fixed using source verification. Another correct statement is that outliers can help determine whether something does not belong in the study. They may reveal invalid records, unusual conditions, a different population, process breakdowns, or rare but important events. The incorrect choices are too absolute. Not all outliers are statistically significant, even under a normal distribution, because significance depends on context, sample size, and method. Likewise, not all observed outliers should be eliminated. Some outliers contain valuable information and can indicate real variation that deserves attention. In quality control, fraud detection, medical screening, and operational monitoring, outliers may be among the most important data points. Therefore, the correct answers are the ones that recognize both correction of miskeyed values and the analytical value of identifying unusual observations.
18. Frage
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?
Antwort: B
Begründung:
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.
19. Frage
Which type of study is also known as a quasi-experimental study?
Antwort: C
Begründung:
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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20. Frage
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