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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation |
| Topic 2: Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance |
| Topic 3: Decision Models & Prescriptive Analytics | 15% | - Decision trees, payoff matrices, expected value - Quality & process improvement tools - Optimization, sensitivity analysis |
| Topic 4: Statistical Concepts & Descriptive Analytics | 25% | - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards |
| Topic 5: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions - Ethics, privacy, and data governance |
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NEW QUESTION # 12
Research data indicate 95% confidence in a study in which subjects who were shown a product advertisement exhibited brand awareness compared to a control group who did not see the advertisement.
What can be concluded from this study?
Answer: C
NEW QUESTION # 13
A plant manager wants to compare the production output for three assembly lines. Why is ANOVA the correct analysis technique to use for this scenario?
Answer: B
Explanation:
ANOVA, or analysis of variance, is the appropriate statistical technique when comparing the means of three or more groups. In this case, the plant manager wants to compare production output for three assembly lines, so ANOVA is the correct method because it can test whether the differences among the group means are statistically significant. It does not directly identify the reason for those differences, nor does it by itself determine the exact production rate mechanism. It also does not simply declare which assembly line has the most output without considering statistical variation. The strength of ANOVA is that it evaluates whether observed differences are likely due to actual process differences rather than random variation. If the ANOVA result is significant, further post hoc analysis may be used to determine which specific lines differ from one another. Therefore, the correct answer is that ANOVA can determine whether there is a significant difference in output among the assembly lines.
NEW QUESTION # 14
The U.S. Postal Service wants to know if local first-class mail is being delivered within two days of postmark.
Which key performance indicator (KPI) should the Postal Service use?
Answer: B
Explanation:
On-time performanceis the most appropriate KPI for measuring whether mail is delivered within a specified timeframe. In data-driven decision making, KPIs must align directly with operational objectives.
The Postal Service's goal is to assess delivery timeliness. On-time performance measures the percentage of mail delivered within the expected service standard, making it a direct and objective indicator.
Customer satisfaction and employee morale provide valuable insights but do not directly measure delivery speed. Incentive performance rate is unrelated to delivery outcomes.
Therefore, the correct answer isC, on-time performance.
NEW QUESTION # 15
What does big data include?
Answer: B
Explanation:
Big data includes both structured and unstructured data. Structured data are organized in predefined formats such as rows and columns in databases, spreadsheets, or transaction systems. Unstructured data include forms such as emails, videos, social media content, images, audio files, sensor outputs, and free-text documents that do not fit neatly into traditional tabular formats. One of the defining features of big data is not just its size, but also its variety. This variety means organizations must work with multiple data types and sources to generate useful insights. The other options do not define what big data includes. Spreadsheets may handle small portions of data, but they do not define the concept itself. Powerful extraction tools may be used in big data environments, but they are tools rather than components of the data. Inferential statistics are analytical methods, not the data itself. Therefore, the best answer is that big data includes both structured and unstructured data.
NEW QUESTION # 16
Why are sample sizes important for ensuring statistical significance?
Answer: D
Explanation:
Sample size is critical for ensuring **statistical significance** because it determines whether results can be confidently generalized to a larger population. In data-driven decision making, larger and appropriately selected samples reduce sampling error and increase the reliability of statistical estimates.
When sample sizes are too small, observed effects may be due to random variation rather than true underlying patterns. Larger samples provide more precise estimates of population parameters and increase the power of hypothesis tests, making it easier to detect meaningful differences or relationships.
While increasing sample size does not eliminate researcher bias, prevent hypothesis misinterpretation, or remove the need for further analysis, it strengthens the validity of conclusions. Statistical significance depends on sample size, effect size, and variability, all of which influence confidence in results.
Therefore, the correct answer is **A**, as adequate sample sizes allow accurate conclusions to be confidently applied to larger populations.
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NEW QUESTION # 17
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