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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Decision Models & Prescriptive Analytics | 15% | - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis - Quality & process improvement tools |
| Topic 2: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods |
| Topic 3: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance - Role of data in business decisions |
| Topic 4: Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance |
| Topic 5: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation |
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NEW QUESTION # 104
A healthcare organization implements a campaign to improve patient satisfaction during recent stays. The average patient satisfaction before the campaign is M = 4.20, SD = 3.0. Six months after the new campaign, the average patient satisfaction is M = 1.5, SD = 2.0.
Which action should the hospital management team take?
Answer: B
Explanation:
Data-driven decision making requires comparing outcomes before and after an intervention to assess effectiveness. In this scenario, the mean patient satisfaction scoredeclined from 4.20 to 1.5following implementation of the campaign, indicating a substantial decrease in satisfaction.
Despite the reduced standard deviation, the large drop in the mean suggests the campaign did not achieve its intended goal and may have negatively affected patient experience. Continuing or modifying the campaign is not justified without evidence of initial improvement or external factors explaining the decline.
Management decisions must be grounded in outcome data rather than assumptions or unrelated metrics such as patient volume. Ethical and effective use of statistics requires discontinuing interventions that demonstrably worsen outcomes.
Therefore, the correct action is todiscontinue the campaign, making optionAthe correct answer.
NEW QUESTION # 105
How is a cost-benefit analysis different in the public and private sectors?
Answer: C
Explanation:
Cost-benefit analysis differs between the public and private sectors primarily because the goals of the two sectors are different. In the public sector, decisions are generally evaluated in terms of the general welfare of the population, including social value, public health, safety, infrastructure, education, and broader community outcomes. In the private sector, cost-benefit analysis is usually more focused on profitability, financial return, efficiency, and shareholder value. While both sectors consider costs and benefits, the definition of "benefit" often changes depending on the mission of the organization. Public-sector benefits may include social improvements that do not generate direct profit, whereas private-sector benefits are often measured through revenue, cost savings, or market performance. The other options are too narrow or incorrect because they suggest one-sided attention to only cost or only benefits. Therefore, the best answer is that the public sector usually focuses on the general welfare of the population, whereas the private sector usually focuses on profits.
NEW QUESTION # 106
For which situation could a scatter diagram be used?
Answer: C
Explanation:
Ascatter diagramis used to visually examine therelationship between two quantitative variables. In data- driven decision making, scatter diagrams help analysts assess whether variables move together, whether the relationship is positive, negative, or nonexistent, and whether the relationship appears linear or nonlinear.
Each point on a scatter diagram represents a paired observation of two variables, such as advertising spend and sales revenue or hours studied and test scores. Patterns in the plotted points can suggest correlation, which may later be explored using regression analysis. Scatter diagrams are exploratory tools and do not, by themselves, establish causation.
A prioritization matrix ranks options, frequency differences are examined using bar or Pareto charts, and differences in means are evaluated using hypothesis tests such as t-tests or ANOVA. Therefore, the correct application of a scatter diagram is to demonstraterelationships between variables, making optionBcorrect.
NEW QUESTION # 107
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: B,C
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 # 108
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: D
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 # 109
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