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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value - Correlation vs. causation |
| Topic 2: 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 3: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution |
| 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% | - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value - Quality & process improvement tools |
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NEW QUESTION # 60
Which type of analysis determines whether there was a significant difference in the average donor solicitation amount between three nonprofit hospital events?
Answer: B
Explanation:
Analysis of Variance (ANOVA)is used to compare the means of three or more groups to determine whether statistically significant differences exist. In data-driven decision making, ANOVA is appropriate when evaluating differences across multiple categories.
In this scenario, the analyst is comparing average donor solicitation amounts across three separate events.
ANOVA tests whether at least one group mean differs from the others.
Cluster analysis groups data, time series examines trends over time, and logistic regression predicts categorical outcomes. Therefore, the correct answer isD, ANOVA.
NEW QUESTION # 61
What classifies analytics as descriptive, predictive, or prescriptive?
Answer: D
Explanation:
Analytics is classified as descriptive, predictive, or prescriptive based onthe purpose of the analysis and the methods used to carry it out, which is a foundational concept in data-driven decision making. The distinction reflects the type of managerial question being addressed rather than technical aspects such as software tools, sample size, or data reliability.
Descriptive analytics focuses on understandingwhat has happenedby summarizing historical data. It relies on descriptive statistics, reports, dashboards, and data visualizations to provide insights into past performance.
Predictive analytics extends this approach to determinewhat is likely to happenby using statistical models, probability distributions, regression analysis, and forecasting techniques to estimate future outcomes.
Prescriptive analytics goes further by identifyingwhat should be doneto achieve desired results. It uses optimization models, decision trees, simulations, and scenario analysis to recommend the best course of action under given constraints.
In data-driven decision making, the classification of analytics depends on how results are intended to support decisions and the analytical techniques applied to achieve that goal. Factors such as data quality and software influence accuracy and efficiency but do not define the analytics category itself. Therefore, the correct classification criterion is thepurpose and methods, making optionCthe correct answer.
NEW QUESTION # 62
Which step in the plan-do-check-act cycle is described as analyzing the results of an experiment and deciding whether those results can be improved?
Answer: B
Explanation:
TheCheckphase of the Plan-Do-Check-Act (PDCA) cycle involves evaluating outcomes and analyzing results. In data-driven decision making, this step compares actual performance against expected results to determine whether objectives were met.
During the Check phase, organizations review data, assess variation, and identify opportunities for improvement. Planning defines objectives, Doing implements changes, and Acting standardizes or adjusts processes based on evaluation.
Therefore, the correct answer isA, Check.
NEW QUESTION # 63
What are random errors caused by?
Answer: C
Explanation:
Random errors are caused by unpredictable fluctuations that occur naturally in measurement, observation, or recording processes. These errors are not consistently in one direction and do not systematically push results higher or lower. Instead, they introduce variability that can make repeated measurements differ slightly even when conditions seem similar. Examples include minor environmental changes, momentary variations in instrument sensitivity, normal human reaction differences, or small observational inconsistencies. Because random errors are unsystematic, they tend to average out over a large number of observations, although they still reduce precision. By contrast, an instrument that needs calibration is more closely associated with systematic error, because it may consistently overstate or understate measurements. Respondents favoring certain outcomes and biased data also reflect systematic forms of bias rather than random variation. In statistics and quality measurement, distinguishing between random error and systematic error is important because each requires a different response. Random error is mainly addressed through repetition, sample size, and statistical controls, whereas systematic error must be corrected at the source. Therefore, the correct cause of random errors is unpredictable fluctuations in readings.
NEW QUESTION # 64
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: C,D
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 # 65
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