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| Section | Weight | Objectives |
|---|---|---|
| Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution |
| Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value - Quality & process improvement tools |
| Probability & Statistical Inference | 20% | - Correlation vs. causation - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value |
| Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions - Ethics, privacy, and data governance |
| Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis |
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NEW QUESTION # 80
How does a balanced scorecard (BSC) differ from a key performance indicator (KPI)?
Answer: A
Explanation:
Akey performance indicator (KPI)measures performance in asingle critical area, such as revenue growth or customer satisfaction. In contrast, abalanced scorecard (BSC)provides amulti-dimensional view of organizational performance, typically across financial, customer, internal process, and learning perspectives.
Data-driven decision making emphasizes that relying on a single metric can lead to incomplete or biased conclusions. The BSC addresses this by integrating multiple KPIs into a cohesive framework aligned with strategic objectives.
Therefore, optionAcorrectly explains the distinction between a KPI and a BSC.
NEW QUESTION # 81
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 # 82
Which use of statistics would apply to employees?
Answer: B
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 # 83
What is the purpose of the quality management principle of dedication to fact-based decision-making?
Answer: C
Explanation:
The principle offact-based decision-makingemphasizes using reliable data and objective analysis rather than intuition or opinion. In data-driven decision making, this principle exists primarily toreduce bias and increase trust in organizational plans and decisions.
When decisions are grounded in verified data, assumptions are challenged, personal biases are minimized, and outcomes are more predictable. This builds confidence among stakeholders and supports transparency and accountability.
Customer loyalty, waste elimination, and quality effectiveness may be indirect benefits, but the core purpose is ensuring that decisions are objective, defensible, and evidence-based. Therefore, the correct answer isD.
NEW QUESTION # 84
Which graphical display is used to examine the distribution of a data set with quartiles?
Answer: A
Explanation:
Aboxplotis specifically designed to display the distribution of a dataset using quartiles. In data-driven decision making, boxplots visually summarize data through the minimum, first quartile, median, third quartile, and maximum.
Boxplots are useful for identifying spread, central tendency, skewness, and potential outliers. Scatterplots and bivariate charts analyze relationships between variables, while Pareto charts rank categorical data by frequency.
Because quartiles are the defining feature of a boxplot, the correct answer isC.
NEW QUESTION # 85
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