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| Section | Weight | Objectives |
|---|---|---|
| Probability & Statistical Inference | 20% | - Correlation vs. causation - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals |
| Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards |
| Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis - Simple & multiple linear regression |
| Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Role of data in business decisions - Types of analytics: descriptive, predictive, prescriptive |
| Decision Models & Prescriptive Analytics | 15% | - Decision trees, payoff matrices, expected value - Quality & process improvement tools - Optimization, sensitivity analysis |
>> Data-Driven-Decision-Making Test Lab Questions <<
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NEW QUESTION # 109
What is a basic assumption of a z-score?
Answer: D
Explanation:
Az-scorestandardizes a value by expressing how many standard deviations it lies from the mean. A fundamental assumption of z-score analysis in data-driven decision making is that the data can be transformed to astandard normal distributionwith amean of zero and a standard deviation of one.
This transformation allows analysts to compare values from different distributions on a common scale and to calculate probabilities using the standard normal table. The formula for a z-score subtracts the mean from the observed value and divides by the standard deviation, resulting in this standardized distribution.
Outliers are not eliminated by default in z-score calculations; instead, z-scores are often used to identify outliers. A standard deviation of 2 is incorrect and would not represent a standardized distribution.
Therefore, the correct answer isA, reflecting the core assumption underlying z-score usage.
NEW QUESTION # 110
A county government wants to start a cost-effective recycling program. How can the county apply data analytic approaches to attain this goal?
Answer: C
Explanation:
To create a cost-effective recycling program, the county should benchmark similar strategies used by other counties. Benchmarking is a practical data-driven approach that allows decision-makers to compare program designs, costs, participation rates, collection methods, and outcomes across jurisdictions facing similar conditions. By examining how other counties structure successful recycling initiatives, the county can identify efficient practices, realistic cost expectations, and implementation strategies that have already been tested in comparable environments. This approach is especially useful when developing a new program because it reduces trial-and-error and supports evidence-based planning. The other options are less directly related to designing and evaluating a recycling program. Analyzing county contracts or aligning funds with staffing levels may be useful in general budgeting, but they do not specifically address recycling strategy effectiveness. Audit performance research is also not closely tied to recycling program design. Therefore, the most appropriate data analytic approach is benchmarking similar strategies of other counties.
NEW QUESTION # 111
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: A
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 # 112
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 # 113
A student is surveying chief executive officers to understand their levels of satisfaction with work-life balance over a period of three years. The student receives responses to a survey with a question regarding how many hours a week each CEO works. Which statistical approach should be used to summarize the data for analysis?
Answer: C
Explanation:
The question asks how to summarize responses about how many hours each CEO works per week. Since hours worked is a numerical variable, a measure of central tendency is appropriate for summarizing the data.
The mean is the standard choice for describing the average number of hours worked across respondents. It provides a single representative value that can be used to understand the general workload pattern in the group. A bell curve is a distribution shape rather than a summary statistic. A scatterplot is a graphical tool used to display relationships between two quantitative variables, not to summarize one variable's central value. The median can also summarize numerical data, especially if there are strong outliers, but the most direct and standard summary for average hours worked is the mean unless the question specifically emphasizes skewness or resistance to outliers. Since the prompt asks for a statistical approach to summarize the data for analysis, the best answer is mean.
NEW QUESTION # 114
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