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| Section | Weight | Objectives |
|---|---|---|
| Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value - Quality & process improvement tools |
| Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis |
| 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 |
| Foundations of Data-Driven Decision Making | 20% | - Role of data in business decisions - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive |
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NEW QUESTION # 109
How does Six Sigma relate to a firm's application of a control chart?
Answer: D
Explanation:
Six Sigma is a quality management methodology focused on reducing defects, minimizing variation, and improving process performance through data-driven analysis. Control charts are one of the statistical process control tools commonly used within Six Sigma because they allow firms to continually measure processes and outputs over time. By tracking data points against control limits, organizations can determine whether a process is operating normally or whether unusual variation requires investigation. This ongoing measurement supports the Six Sigma goal of achieving highly consistent performance and identifying sources of error early.
The other options do not accurately describe the relationship. Six Sigma is not equivalent to linear programming, which is an optimization technique. It also does not directly produce a balanced scorecard, which is a broader strategic performance framework. Although supplier and input reliability may matter in quality management, that is not the primary connection between Six Sigma and control charts. Therefore, the best answer is that Six Sigma continually measures processes and outputs through tools such as control charts.
NEW QUESTION # 110
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: A,D
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 # 111
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: C,D
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 # 112
Which use of statistics would apply to employees?
Answer: D
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 # 113
Research data indicate 95% confidence in a study in which subjects who were shown a product advertisement exhibited brand awareness compared to a control group who did not see the advertisement.
What can be concluded from this study?
Answer: D
NEW QUESTION # 114
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