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| Section | Objectives |
|---|---|
| Topic 1: Data Analysis Fundamentals | - Data Types and Measurement Scales - Descriptive Statistics - Data Visualization Techniques - Data Quality and Cleaning |
| Topic 2: Business Intelligence and Analytics | - Predictive Analytics Basics - Dashboards and Reporting - Key Performance Indicators (KPIs) - Data Mining Concepts |
| Topic 3: Decision-Making Frameworks | - Risk Analysis and Assessment - Evidence-Based Decision Making - Cost-Benefit Analysis - Rational Decision Models |
| Topic 4: Data-Driven Culture and Communication | - Stakeholder Communication - Building Data-Informed Organizations - Presenting Data Insights - Ethical Considerations in Data Use |
>> Data-Driven-Decision-Making Reliable Study Notes <<
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NEW QUESTION # 11
What does big data include?
Answer: A
Explanation:
Big data includes both structured and unstructured data. Structured data are organized in predefined formats such as rows and columns in databases, spreadsheets, or transaction systems. Unstructured data include forms such as emails, videos, social media content, images, audio files, sensor outputs, and free-text documents that do not fit neatly into traditional tabular formats. One of the defining features of big data is not just its size, but also its variety. This variety means organizations must work with multiple data types and sources to generate useful insights. The other options do not define what big data includes. Spreadsheets may handle small portions of data, but they do not define the concept itself. Powerful extraction tools may be used in big data environments, but they are tools rather than components of the data. Inferential statistics are analytical methods, not the data itself. Therefore, the best answer is that big data includes both structured and unstructured data.
NEW QUESTION # 12
How is a cause-and-effect diagram used?
Answer: A
Explanation:
A cause-and-effect diagram, also known as an Ishikawa diagram or fishbone diagram, is used to brainstorm and organize possible root causes of a problem. It helps teams think systematically about why a problem may be occurring by grouping causes into logical categories such as methods, materials, people, equipment, and environment. In the scenario given, an intermittency problem suggests an issue that happens irregularly and may be difficult to isolate, which makes a cause-and-effect diagram especially useful. Its purpose is not to assign blame but to support structured problem-solving and root cause analysis. It is also not a financial risk tool or an automated software diagram generator. In quality management and data-driven improvement, this diagram encourages teams to explore multiple contributing factors before deciding on corrective action.
Therefore, the correct answer is that it is used to brainstorm possible root causes for an intermittency problem.
NEW QUESTION # 13
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 # 14
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: C,D
Explanation:
Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is that more time is spent managing data than analyzing data. Analysts must devote substantial effort to cleaning, validating, and correcting errors before meaningful analysis can occur, delaying insights and increasing costs.
Another critical result is that missing data tend to skew the results of the analysis. Incomplete data can distort averages, trends, and statistical relationships, leading to biased conclusions and unreliable decisions. This is especially problematic in predictive and inferential analytics, where assumptions about data completeness are essential.
Using spreadsheets or placing data in charts does not inherently result from flawed data, nor does it resolve data quality issues. While visualization can help identify errors, it is not a direct outcome of starting with flawed data.
Data-driven decision making emphasizes that poor-quality input leads to poor-quality output. Ensuring data accuracy and completeness before analysis is essential for producing valid insights. Therefore, the correct answers are B and D.
NEW QUESTION # 15
A nonprofit organization ran a fundraiser and would like to determine the amount of a typical donation.
Which statistic is less affected by outliers and skewed data and should be used to determine the amount of a typical donation?
Answer: B
Explanation:
In data-driven decision making, themedianis the preferred measure of central tendency when data contain outliers or are skewed. Fundraising donation amounts often exhibit right-skewed distributions, where a small number of very large donations can significantly inflate the mean. Using the mean in such cases may misrepresent what a "typical" donor gives.
The median represents the middle value when donation amounts are ordered from smallest to largest. Because it depends only on position rather than magnitude, it isrobust to extreme values. This makes it especially useful for summarizing typical behavior in skewed financial data.
The mean is sensitive to outliers, the z-score measures standardized distance from the mean, and the mode identifies the most frequent value but may not reflect central tendency in continuous donation data. Therefore, the statistic that best represents a typical donation amount is themedian, making optionCcorrect.
NEW QUESTION # 16
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