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| Section | Weight | Objectives |
|---|---|---|
| Decision Models & Prescriptive Analytics | 15% | - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis - Quality & process improvement tools |
| Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression |
| 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% | - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions |
| Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution |
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NEW QUESTION # 32
When researchers are studying the effect of new drug treatments on patients, bias can be introduced by patients if they are aware of who receives the placebo.
Which type of research design eliminates this type of bias?
Answer: B
Explanation:
Ablind studyis specifically designed to eliminate bias that occurs when participants are aware of treatment assignments. In data-driven decision making and experimental research, patient awareness of receiving a placebo or treatment can influence reported symptoms, perceived effectiveness, and behavior, thereby biasing results.
In a blind study, participants do not know whether they are receiving the treatment or the placebo. This prevents expectations or beliefs from influencing outcomes and ensures that observed effects are attributable to the treatment itself rather than psychological or behavioral factors.
Observational studies and prospective cohort studies do not involve controlled assignment of treatments and therefore cannot eliminate this type of bias. Time series studies analyze data over time but do not address participant awareness of treatment allocation.
By preventing patients from knowing their treatment group, blind studies improve internal validity and support more accurate causal inference. Therefore, the correct answer isD, blind study.
NEW QUESTION # 33
What is the basic difference between evaluating costs and benefits in the public and private sectors?
Answer: C
Explanation:
The fundamental difference between cost-benefit evaluation in the public and private sectors lies inhow benefits are defined and measured. In data-driven decision making, private-sector projects primarily focus onrevenue generation and profitability, making optionCthe correct distinction.
Private organizations evaluate benefits using measurable financial outcomes such as revenue, profit margins, and return on investment. These metrics provide clear, quantifiable indicators of success. In contrast, public- sector projects often aim to maximizegeneral public welfare, including social, environmental, and economic benefits that are more difficult to quantify monetarily.
Public-sector benefits may include improved public health, safety, education, or trust in government- outcomes that do not translate directly into revenue. Therefore, while costs are measurable in both sectors, benefits differ substantially in nature.
Options A and B are incorrect because public-sector costs are not minimal and public benefits are often difficult to quantify. Option D incorrectly assigns public welfare to private projects. Thus, the correct answer isC.
NEW QUESTION # 34
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 # 35
Management uses a net promoter score.
What can management determine using this performance measurement?
Answer: B
Explanation:
Thenet promoter score (NPS)measurescustomer loyaltyby assessing the likelihood that customers will recommend a company's products or services to others. In data-driven decision making, NPS is a widely used indicator of customer satisfaction and long-term growth potential.
Customers are typically asked how likely they are to recommend the organization on a numerical scale.
Responses are categorized into promoters, passives, and detractors, and the score is calculated by subtracting the percentage of detractors from the percentage of promoters.
NPS does not directly measure financial outcomes, employee performance, or quality assurance metrics.
Instead, it serves as a customer-focused indicator that reflects overall perception and loyalty.
Therefore, the correct answer isB.
NEW QUESTION # 36
A manager has been asked to evaluate the risk of loss for a new business strategy. The manager plots the results of several simulated projections to determine the likelihood of a result being a loss. Which statistic will transform different data sets to the same scale so that the manager can compare the projections?
Answer: C
Explanation:
A z score is used to standardize values from different data sets so they can be compared on the same scale. It expresses how far a value lies from the mean in terms of standard deviations. This makes it especially useful when a manager needs to compare simulated projections that may have different averages and different spreads. By converting the results into z scores, the manager can evaluate relative performance and risk across otherwise non-comparable distributions. Median and mode describe central tendency, but they do not place values on a common standardized scale. Variance measures dispersion, but it does not directly convert or normalize observations for comparison. In risk analysis and simulation-based decision-making, standardization is often necessary when results come from multiple scenarios, models, or assumptions. Z scores provide that standard frame of reference and allow meaningful interpretation of whether a projected loss is unusually high, low, or typical within its own distribution. Therefore, the correct answer is z score because it transforms different data sets to a common comparison scale.
NEW QUESTION # 37
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