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| Section | Objectives |
|---|---|
| Topic 1: Business Intelligence and Analytics | - Dashboards and Reporting - Data Mining Concepts - Key Performance Indicators (KPIs) - Predictive Analytics Basics |
| Topic 2: Decision-Making Frameworks | - Risk Analysis and Assessment - Rational Decision Models - Cost-Benefit Analysis - Evidence-Based Decision Making |
| Topic 3: Data Analysis Fundamentals | - Data Types and Measurement Scales - Descriptive Statistics - Data Visualization Techniques - Data Quality and Cleaning |
| Topic 4: Data-Driven Culture and Communication | - Presenting Data Insights - Stakeholder Communication - Building Data-Informed Organizations - Ethical Considerations in Data Use |
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NEW QUESTION # 114
Which tool is often referred to as a fishbone chart or diagram?
Answer: C
Explanation:
Thecause-and-effect diagram, also known as thefishbone diagramorIshikawa diagram, is used to identify potential causes of a problem. In data-driven decision making, it helps teams systematically explore root causes rather than focusing on symptoms.
The diagram visually organizes possible causes into categories such as people, processes, materials, machines, and environment. Histograms show distributions, flowcharts depict process steps, and scatter diagrams show relationships between variables.
Therefore, the correct answer isC, cause-and-effect diagram.
NEW QUESTION # 115
A car dealership sells both new and used cars. The number of new cars sold on a given day ranges from 5 to
30 while the number of used cars sold ranges from 5 to 40. The number of used cars sold is mutually exclusive to the number of new cars sold.
Which statistic would be used to compare the number of new and used car sales on any given day?
Answer: C
Explanation:
Thechi-square statisticis used to compare frequencies of categorical, mutually exclusive outcomes. In data- driven decision making, it is appropriate for analyzing differences between observed counts.
New and used car sales represent mutually exclusive categories, making chi-square the correct choice.
Therefore, the correct answer isB.
NEW QUESTION # 116
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: A,C
Explanation:
Good data quality management plays a critical role in improving business decision-making by ensuring that data is accurate, complete, and reliable. One key benefit is that itensures there are no missing data points, which helps maintain data completeness. Missing data can distort results, reduce analytical power, and lead to incorrect conclusions, especially in descriptive and inferential statistics.
Another important benefit is that data quality managementmitigates undetected errors from the data-entry process. Errors such as duplicate entries, incorrect values, or inconsistent formats can significantly bias analysis if left unnoticed. Through validation checks, cleaning procedures, and governance standards, organizations reduce the risk of flawed insights.
While good data quality supports better analysis, it does not guarantee statistical significance, as significance depends on sample size, variability, and study design. Similarly, it does not necessarily make the statistical process faster; in fact, data cleaning can be time-consuming. However, it improves theaccuracy and trustworthinessof outcomes.
In data-driven decision making, high-quality data is essential because decisions are only as good as the data used to support them. Therefore, the correct answers areA and D.
NEW QUESTION # 117
Which two tools make it easier to detect an out-of-range error?
Choose 2 answers.
Answer: B,C
Explanation:
Out-of-range errors occur when a data value falls outside the allowable or expected limits for a variable.
Examples include a negative age, a score above the maximum possible value, or a date in an impossible format. The tools most useful for identifying such errors are relational databases and spreadsheets. Relational databases often include validation rules, field constraints, data types, and query capabilities that can detect impossible or invalid entries. For example, a database can restrict a field to numeric values within a set range or flag records that violate defined rules. Spreadsheets can also support error detection through conditional formatting, formulas, filters, data validation, and sorting features that make unusual values easier to spot.
Experimental studies and observational studies are research designs, not data-validation tools. They describe how data are collected, not how errors are detected in stored records. Because the question asks specifically for tools that make out-of-range errors easier to detect, the correct choices are the data-handling tools that support validation and review: relational databases and spreadsheets.
NEW QUESTION # 118
A patient satisfaction survey was conducted at Family Practice A. The average rating of online telemedicine visits was 4.5 out of 5, while in-person visits received a 3.0 out of 5.
Which samples should be used to compare the ratings?
Answer: D
Explanation:
To make a valid comparison in data-driven decision making, samples must becomparable and drawn from the same population, differing only in the factor being evaluated. In this case, the goal is to compare patient satisfaction between online telemedicine visits and in-person visits atFamily Practice A.
Usingonline Family Practice A telemedicine visits and in-person Family Practice A visitsensures that both samples come from the same organization, patient base, and survey methodology. This controls for external factors such as practice standards, demographics, and survey design, allowing differences in ratings to be attributed to the visit type rather than unrelated variables.
Comparing total visits to only one visit type introduces imbalance. Including other family practices introduces external variation and invalidates the comparison. Data-driven decision making stresses consistency and relevance in sample selection to ensure accurate conclusions.
Therefore, the correct answer isD, as it uses comparable samples that isolate the variable of interest.
NEW QUESTION # 119
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