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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation - Probability rules, distributions, expected value |
| Topic 2: Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Quality & process improvement tools - Decision trees, payoff matrices, expected value |
| Topic 3: Foundations of Data-Driven Decision Making | 20% | - Role of data in business decisions - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive |
| Topic 4: Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards |
| Topic 5: Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance |
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NEW QUESTION # 61
Two vendors sell a dozen raw materials that a company needs to build its products. Which type of analysis should be used to determine if one of the vendors has a better average price at a given point in time?
Answer: B
Explanation:
To determine whether one vendor has a better average price than another, the company should use data analysis. This is the broad analytical approach that involves collecting pricing information, organizing the values, comparing averages, and possibly applying additional statistical methods to evaluate the difference.
The task is numerical and evidence-based, making data analysis the correct and most complete choice.
Heuristic analysis usually refers to rule-of-thumb decision methods rather than direct statistical comparison.
Background analysis is not the standard term for comparing measurable pricing information. Graphical analysis may help visualize price differences, but graphs alone do not fully describe the method needed to determine average pricing performance. Because the question asks which type of analysis should be used to compare average price, the answer is the one that directly refers to analyzing the numerical data itself.
Therefore, the correct answer is data analysis.
NEW QUESTION # 62
A political ballot gives voters the option to vote for one of three candidates. Eight voters cast their ballots.
Which statistical rule should be used to determine the possible voting outcomes?
Answer: A
Explanation:
Themultiplication principleis used to determine the number of possible outcomes when multiple independent choices occur in sequence. In data-driven decision making and probability theory, this rule applies when each event has a fixed number of outcomes and each outcome is independent of the others.
In this scenario, each of the eight voters can independently choose one of three candidates. The total number of possible voting outcomes is calculated by multiplying the number of choices available for each voter.
Because the voters act independently and order matters in counting outcomes, the multiplication principle is the correct method.
Conditional probability applies when outcomes depend on prior events, Bayes' theorem updates probabilities based on new information, and combinations are used when order does not matter. None of these fit the structure of this problem.
Therefore, the correct answer isA, multiplication principle.
NEW QUESTION # 63
What is true about outliers?
Choose 2 answers.
Answer: A,C
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 # 64
What is an omission error?
Answer: B
Explanation:
Anomission erroroccurs whencrucial data is missingfrom a dataset, which can significantly compromise the quality of analysis and decision-making. In data-driven decision making, omission errors are a serious concern because missing information can lead to biased results, incorrect interpretations, and flawed conclusions.
Omission errors may arise during data collection, data entry, or data integration processes. For example, failing to record customer demographics, transaction values, or time periods can distort descriptive statistics and weaken predictive models. Unlike inaccuracies, which involve incorrect values, omission errors involve the absence of necessary data altogether.
Outliers represent extreme values and are not omission errors. Similarly, failing to review all data is a process issue rather than a data-quality error definition. Inaccurate data refers to incorrect or erroneous values, not missing ones.
Effective data quality management emphasizes identifying and correcting omission errors through validation rules, completeness checks, and data audits. In data-driven decision making, ensuring that all relevant data is captured is essential for producing reliable insights and supporting sound business decisions. Therefore, the correct answer isD, as an omission error occurs when crucial data is missing.
NEW QUESTION # 65
Two project teams are assigned to upgrade an on-premise data warehouse to a cloud-based data lake in 13 months. The infrastructure team has five team members, while the enterprise analytics team has three team members. The enterprise analytics team cannot move into production until the infrastructure team has completed the migration.
What should be used to find the probability that the project will be completed on time?
Answer: D
Explanation:
This scenario requires the use of **conditional probability**, which applies when the likelihood of one event depends on the occurrence of another event. In data-driven decision making, conditional probability is used to model dependent events within processes, workflows, and project timelines.
In this case, the enterprise analytics team's ability to move into production is **dependent on** the infrastructure team completing the migration. Because one event cannot occur unless another event has already occurred, the probability of completing the project on time must account for this dependency.
The multiplication principle applies to independent events, Bayes' theorem updates probabilities based on new information, and combinations are used for counting outcomes, not dependency analysis. Conditional probability explicitly captures the relationship between dependent tasks.
Project risk analysis and scheduling often rely on conditional probability to assess completion likelihood when tasks are sequentially linked. Therefore, the correct answer is **C**, conditional probability.
NEW QUESTION # 66
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