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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 |
| Statistical Concepts & Descriptive Analytics | 25% | - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards |
| Probability & Statistical Inference | 20% | - Correlation vs. causation - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals |
| 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 - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance |
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NEW QUESTION # 72
Which step in the plan-do-check-act cycle is described as analyzing the results of an experiment and deciding whether those results can be improved?
Answer: B
Explanation:
TheCheckphase of the Plan-Do-Check-Act (PDCA) cycle involves evaluating outcomes and analyzing results. In data-driven decision making, this step compares actual performance against expected results to determine whether objectives were met.
During the Check phase, organizations review data, assess variation, and identify opportunities for improvement. Planning defines objectives, Doing implements changes, and Acting standardizes or adjusts processes based on evaluation.
Therefore, the correct answer isA, Check.
NEW QUESTION # 73
A student with a degree is presumed to already have a bachelor's degree. Which type of data does this represent?
Answer: D
Explanation:
This question refers to categorizing information rather than measuring it numerically. The phrase "a student with a degree" identifies a classification or label, not a value with mathematical meaning. Nominal data are used to place observations into distinct categories without any inherent numerical order or ranking. In this case, the student is being grouped according to degree status, which is a named category. Interval and ratio data are numerical measurement scales, so they do not apply here. Ordinal data involve ranked categories, such as low, medium, and high, or freshman through senior, where order matters. Here, the information does not describe rank or position; it simply identifies a class of person based on a characteristic. Even though the phrase mentions a bachelor's degree, the key issue is that the information is categorical rather than numeric.
Therefore, this is best understood as nominal data. In data analysis, recognizing nominal variables is important because they are usually summarized with counts, percentages, or category-based comparisons rather than means or standard deviations.
NEW QUESTION # 74
What is the 80/20 rule associated with a Pareto chart?
Answer: B
Explanation:
The80/20 rule, or Pareto principle, states that80% of problems result from 20% of causes. In data-driven decision making, this concept helps organizations prioritize efforts where they will have the greatest impact.
Pareto charts visually demonstrate this principle by showing that a small number of factors contribute disproportionately to overall problems. By addressing these critical few causes, organizations can achieve significant improvements with focused effort.
Therefore, the correct answer isD.
NEW QUESTION # 75
Which input could be used to create and evaluate a process that would improve an organization's performance?
Answer: C
Explanation:
Data gathered from a customer survey can be used to create and evaluate a process that improves organizational performance because customer feedback provides direct evidence about satisfaction, service quality, unmet needs, and areas requiring change. In data-driven decision-making, effective process improvement begins with relevant and reliable inputs, and customer survey data are especially valuable because they reflect the experiences of the people receiving the organization's products or services. These data can reveal trends, pain points, expectations, and opportunities for redesign. Suggestions from a competitor may be informative, but they are not as direct or reliable as structured internal evidence gathered from actual stakeholders. Information from an employee handbook outlines policies rather than performance inputs, and industry regulations define compliance boundaries rather than improvement opportunities. Since the question asks which input can help create and evaluate a better-performing process, the most appropriate answer is data gathered from a customer survey.
NEW QUESTION # 76
Why are experiments conducted using random sample populations?
Answer: C
Explanation:
Experiments and studies commonly use random samples because observing an entire population is often too difficult, costly, time-consuming, or impractical. In most real-world settings, researchers cannot collect data from every individual, item, or event of interest. A properly selected random sample allows them to estimate population characteristics with a manageable amount of effort while still preserving the ability to make statistical inferences. Random sampling improves representativeness and reduces selection bias, but it does not guarantee total elimination of bias. That is why the option claiming bias elimination is incorrect.
Likewise, the issue is not mainly about outliers or software cost. The main reason for using random samples is feasibility combined with inferential validity. With sound sampling methods, researchers can use probability theory to generalize findings from the sample to the broader population and estimate the degree of uncertainty in those conclusions. Therefore, the strongest and most accurate answer is that studying the entire population is difficult and impractical, making random sampling the preferred and efficient alternative.
NEW QUESTION # 77
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