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| Section | Weight | Objectives |
|---|---|---|
| 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 visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods |
| Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression |
| Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value |
| Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation |
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NEW QUESTION # 85
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: A,B
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 # 86
A student is surveying chief executive officers to understand their levels of satisfaction with work-life balance over a period of three years. The student receives responses to a survey with a question regarding how many hours a week each CEO works. Which statistical approach should be used to summarize the data for analysis?
Answer: C
Explanation:
The question asks how to summarize responses about how many hours each CEO works per week. Since hours worked is a numerical variable, a measure of central tendency is appropriate for summarizing the data.
The mean is the standard choice for describing the average number of hours worked across respondents. It provides a single representative value that can be used to understand the general workload pattern in the group. A bell curve is a distribution shape rather than a summary statistic. A scatterplot is a graphical tool used to display relationships between two quantitative variables, not to summarize one variable's central value. The median can also summarize numerical data, especially if there are strong outliers, but the most direct and standard summary for average hours worked is the mean unless the question specifically emphasizes skewness or resistance to outliers. Since the prompt asks for a statistical approach to summarize the data for analysis, the best answer is mean.
NEW QUESTION # 87
Which element is associated with control charts?
Answer: B
Explanation:
Control charts are a core tool in statistical process control and are used to monitor variation in a process over time. One of their defining features is the inclusion of upper and lower control limits. These limits help determine whether process variation is consistent with common-cause variation or whether unusual, assignable causes may be affecting performance. The chart typically includes a center line representing the process average, along with upper and lower limits that establish the acceptable range of variation under stable conditions. When points fall outside these limits or show nonrandom patterns, the process may require investigation. While hypothesis testing and correlation are important statistical concepts, they are not the primary identifying elements of a control chart. A reliability index may be used in certain engineering or quality contexts, but it is not the standard feature that defines control charts. Therefore, the element most directly associated with control charts is upper and lower limits, since those boundaries are central to evaluating whether a process remains in statistical control over time.
NEW QUESTION # 88
Phone calls for a company are routed randomly to one of eight call centers. Six are based in the United States, and two are based in another country. What is the probability that an incoming call will be routed to a U.S.- based call center?
Answer: C
Explanation:
Probability is calculated as the number of favorable outcomes divided by the total number of possible outcomes, assuming each outcome is equally likely. In this case, there are eight call centers total, and six of them are located in the United States. Since calls are routed randomly, each call center has an equal chance of receiving an incoming call. Therefore, the probability that a call is routed to a U.S.-based call center is 6 out of 8. This fraction simplifies to 3 out of 4, which is equal to 0.75 or 75 percent. The answer choices 25 percent and 33 percent are too small because they do not match the proportion of U.S. call centers. The option 67 percent is closer but still incorrect, as 6 divided by 8 is not 0.67. This is a basic probability problem involving equally likely outcomes. Because six of the eight centers are in the United States, the correct probability is 75 percent.
NEW QUESTION # 89
A hospital is restructuring its business and administrative functions.
Which component of service delivery could be analyzed with a data analytics approach to help determine whether the hospital is adequately staffed for each shift?
Answer: B
Explanation:
Patient-to-staff ratiosare a critical analytic measure for determining whether a hospital is adequately staffed for each shift. In data-driven decision making, staffing adequacy is best assessed by examining workload demand relative to available personnel.
Patient-to-staff ratios directly reflect how many patients each staff member is responsible for during a given shift. High ratios may indicate understaffing, increased risk of burnout, and reduced quality of care, while lower ratios suggest more manageable workloads and better patient outcomes.
Staff productivity levels measure efficiency but do not directly capture demand. Education levels reflect qualifications rather than staffing sufficiency. Patient satisfaction is an outcome metric and may be influenced by many factors beyond staffing levels.
By analyzing patient-to-staff ratios across shifts, hospital administrators can identify imbalances, allocate resources more effectively, and improve operational efficiency. Therefore, the correct answer isB.
NEW QUESTION # 90
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