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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Role of data in business decisions - Types of analytics: descriptive, predictive, prescriptive |
| Topic 2: Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance |
| Topic 3: Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Quality & process improvement tools - Decision trees, payoff matrices, expected value |
| Topic 4: Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution |
| Topic 5: Probability & Statistical Inference | 20% | - Correlation vs. causation - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value |
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NEW QUESTION # 39
What classifies analytics as descriptive, predictive, or prescriptive?
Answer: D
Explanation:
Analytics is classified as descriptive, predictive, or prescriptive based onthe purpose of the analysis and the methods used to carry it out, which is a foundational concept in data-driven decision making. The distinction reflects the type of managerial question being addressed rather than technical aspects such as software tools, sample size, or data reliability.
Descriptive analytics focuses on understandingwhat has happenedby summarizing historical data. It relies on descriptive statistics, reports, dashboards, and data visualizations to provide insights into past performance.
Predictive analytics extends this approach to determinewhat is likely to happenby using statistical models, probability distributions, regression analysis, and forecasting techniques to estimate future outcomes.
Prescriptive analytics goes further by identifyingwhat should be doneto achieve desired results. It uses optimization models, decision trees, simulations, and scenario analysis to recommend the best course of action under given constraints.
In data-driven decision making, the classification of analytics depends on how results are intended to support decisions and the analytical techniques applied to achieve that goal. Factors such as data quality and software influence accuracy and efficiency but do not define the analytics category itself. Therefore, the correct classification criterion is thepurpose and methods, making optionCthe correct answer.
NEW QUESTION # 40
Which use of statistics would apply to employees?
Answer: C
Explanation:
Statistics can be applied to employees and organizations in many ways, but among the choices given, predicting future levels of financial risk is the best fit for a practical statistical use. Organizations often use statistical models to evaluate uncertainty related to staffing, benefits, payroll obligations, productivity changes, turnover, insurance exposure, and broader business performance. These analyses help managers make more informed decisions about budgeting, hiring, workforce planning, and operational resilience. The other options are less directly tied to employee-related statistical application. Influencing vendor prices and comparing wholesale pricing are more related to procurement and market analysis than to employees.
Determining financial interest rates generally falls under financial markets, lending, or macroeconomic policy rather than an employee-centered use of statistics. In a data-driven environment, statistical tools are frequently used to forecast risk and evaluate future scenarios so that organizations can protect resources and plan responsibly. Therefore, predicting future levels of financial risk is the most accurate answer because it reflects a recognized analytical application of statistics within organizational decision-making.
NEW QUESTION # 41
A hospital is restructuring its business and administrative functions.
Which component of service delivery could be analyzed with a data analytics approach to help determine whether the hospital is adequately staffed for each shift?
Answer: A
Explanation:
Patient-to-staff ratiosare a critical analytic measure for determining whether a hospital is adequately staffed for each shift. In data-driven decision making, staffing adequacy is best assessed by examining workload demand relative to available personnel.
Patient-to-staff ratios directly reflect how many patients each staff member is responsible for during a given shift. High ratios may indicate understaffing, increased risk of burnout, and reduced quality of care, while lower ratios suggest more manageable workloads and better patient outcomes.
Staff productivity levels measure efficiency but do not directly capture demand. Education levels reflect qualifications rather than staffing sufficiency. Patient satisfaction is an outcome metric and may be influenced by many factors beyond staffing levels.
By analyzing patient-to-staff ratios across shifts, hospital administrators can identify imbalances, allocate resources more effectively, and improve operational efficiency. Therefore, the correct answer isB.
NEW QUESTION # 42
Which performance metric simultaneously accounts for financial, customer, internal process, and learning metrics?
Answer: A
Explanation:
Thebalanced scorecard (BSC)is a performance management framework that simultaneously accounts for financial, customer, internal process, and learning and growth metrics. In data-driven decision making, the balanced scorecard provides a holistic view of organizational performance rather than focusing on a single dimension of success.
Financial metrics assess profitability and sustainability, customer metrics evaluate satisfaction and loyalty, internal process metrics examine operational efficiency, and learning and growth metrics focus on employee development and innovation. By integrating these perspectives, the balanced scorecard ensures alignment between day-to-day operations and long-term strategic goals.
Customer complaint reports, income statements, and balance sheets each address only one aspect of performance. They do not provide the multi-dimensional insight necessary for strategic decision-making.
Therefore, the correct answer isA, balanced scorecard.
NEW QUESTION # 43
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: B,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 # 44
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