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| Section | Objectives |
|---|---|
| Decision-Making Frameworks | - Rational Decision Models - Evidence-Based Decision Making - Risk Analysis and Assessment - Cost-Benefit Analysis |
| Data-Driven Culture and Communication | - Stakeholder Communication - Ethical Considerations in Data Use - Presenting Data Insights - Building Data-Informed Organizations |
| Data Analysis Fundamentals | - Data Quality and Cleaning - Data Types and Measurement Scales - Descriptive Statistics - Data Visualization Techniques |
| Business Intelligence and Analytics | - Dashboards and Reporting - Data Mining Concepts - Key Performance Indicators (KPIs) - Predictive Analytics Basics |
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NEW QUESTION # 108
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: C
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 # 109
Which process is considered a statistical process control activity?
Answer: B
Explanation:
Statistical process control involves using statistical methods to monitor, control, and improve production processes. A central purpose of this approach is to determine whether a process is operating within acceptable variation limits. Therefore, determining if the precision of a manufactured product is within a tolerable range is a direct example of statistical process control. This type of activity uses measured data to evaluate consistency, detect abnormal variation, and maintain quality standards. Evaluating consumer complaints may provide useful quality feedback, but it is not the direct statistical monitoring activity described by process control methods. Aligning marketing strategy with manufacturing capabilities is a business planning issue rather than a process control task. Forecasting future consumer demand is a forecasting and analytics activity, not statistical process control. The defining feature of statistical process control is monitoring production behavior using measurable process data and tolerance limits. Therefore, the correct answer is the option that focuses on determining whether the manufactured product's precision remains within an acceptable range.
NEW QUESTION # 110
Which two characteristics must a researcher consider concerning data quality when ensuring that an analysis is based on a clean data set?
Choose 2 answers.
Answer: C,D
Explanation:
When evaluating whether a data set is clean enough for analysis, a researcher must focus on data quality dimensions that directly affect validity and usefulness. Two important characteristics are uniqueness and relevance. Data elements must be unique to prevent duplicate records from distorting counts, averages, totals, and trend analyses. Duplicate entries can lead to biased results, especially in customer, transaction, or survey data. Relevance is equally important because even accurate data are not helpful if they do not pertain to the question being studied. A clean data set should support the actual purpose of the analysis rather than merely being complete or large. The statement about age is incorrect because timeliness often matters; outdated data may no longer reflect the current environment. The statement that data cannot contain outliers is also too absolute. Outliers may be valid observations and can sometimes reveal important conditions, anomalies, or data-entry problems that require investigation rather than automatic removal. Thus, the best two characteristics are uniqueness and relevance, because both directly support meaningful, accurate, and decision- ready analysis.
NEW QUESTION # 111
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: A
NEW QUESTION # 112
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: A,C
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 # 113
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