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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution |
| Topic 2: Probability & Statistical Inference | 20% | - Correlation vs. causation - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value |
| Topic 3: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions - Ethics, privacy, and data governance |
| Topic 4: Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis - Simple & multiple linear regression |
| Topic 5: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
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NEW QUESTION # 24
What is the purpose of the quality management principle of dedication to fact-based decision-making?
Answer: B
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 # 25
A sample of 1,020 people was asked how many minutes they exercise on a typical day. The data were plotted and determined to beskewed leftwith a mean of 44.75 minutes.
Which boxplot correctly graphs this data?
Answer: B
NEW QUESTION # 26
What is true about outliers?
Choose 2 answers.
Answer: B,D
Explanation:
Outliers are observations that differ substantially from the rest of the data, but they are not automatically errors and should not be removed without investigation. One correct statement is that miskeyed outliers can be corrected before analysis. For example, if a value of 500 is entered instead of 50, this is a data-entry error, not a meaningful observation, and it should be fixed using source verification. Another correct statement is that outliers can help determine whether something does not belong in the study. They may reveal invalid records, unusual conditions, a different population, process breakdowns, or rare but important events. The incorrect choices are too absolute. Not all outliers are statistically significant, even under a normal distribution, because significance depends on context, sample size, and method. Likewise, not all observed outliers should be eliminated. Some outliers contain valuable information and can indicate real variation that deserves attention. In quality control, fraud detection, medical screening, and operational monitoring, outliers may be among the most important data points. Therefore, the correct answers are the ones that recognize both correction of miskeyed values and the analytical value of identifying unusual observations.
NEW QUESTION # 27
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: C
NEW QUESTION # 28
Amusement Park W is in California. Amusement Park X is in Texas. A survey asks 1,000 people living in California if they prefer Amusement Park W or X.
Which problem exists with this survey?
Answer: B
Explanation:
The primary problem with this survey issystematic error, which occurs when the data collection process consistently favors certain outcomes due to flawed design. In data-driven decision making, systematic error arises when a sampling method introduces bias that skews results in a predictable direction.
In this scenario, surveying only people living in California creates a location-based bias. Respondents are far more likely to prefer Amusement Park W because it is geographically closer, more familiar, and more accessible than Amusement Park X in Texas. This bias does not occur randomly; instead, it systematically influences responses toward one option, making the results unreliable for comparing overall preferences between the two parks.
Random error would involve unpredictable variation, which is not the issue here. Measurement bias relates to how questions are asked or measured, and information bias concerns inaccurate or misleading data reporting.
The core issue is thenon-representative sample, which violates the principle of unbiased data collection.
Data-driven decision making emphasizes that valid conclusions require representative samples. Because the survey design inherently favors one outcome, the results cannot be generalized, makingsystematic errorthe correct answer.
NEW QUESTION # 29
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