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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Foundations of Data-Driven Decision Making | 20% | - Role of data in business decisions - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive |
| Topic 2: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
| Topic 3: Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance |
| Topic 4: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution |
| Topic 5: Probability & Statistical Inference | 20% | - Correlation vs. causation - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value |
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NEW QUESTION # 75
What is a primary objective of the Six Sigma quality management system?
Answer: C
Explanation:
A primary objective of Six Sigma is to reduce defects and process variation so extensively that operations move toward near-perfect performance. This is why the best answer is approaching perfection in manufacturing operations. Six Sigma is built on the idea that consistent measurement, disciplined process improvement, and statistical control can dramatically improve quality and efficiency. The goal is not absolute perfection in a literal sense, but very low defect rates and highly reliable outcomes. Producing a balanced scorecard is unrelated to the main objective of Six Sigma, as that is a strategic performance measurement framework. Establishing ISO 9000 standards is also different; ISO standards relate to quality system requirements, while Six Sigma is a methodology for improvement and defect reduction. Although Six Sigma projects may help managers organize improvement efforts, its primary purpose is not simply to provide a roadmap. Its central mission is achieving superior quality performance through continuous reduction of errors and variability. Therefore, the correct answer is approaching perfection in manufacturing operations.
NEW QUESTION # 76
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 # 77
A researcher seeks to pass a bond issue and asks a sample of respondents who have a bachelor's degree if they are voting in favor of the bond because it would be beneficial to the county.
Which type of error does this represent?
Answer: D
Explanation:
This scenario represents **selection bias**, which occurs when a sample is not representative of the population being studied. In data-driven decision making, valid conclusions depend on collecting data from a sample that accurately reflects the broader population.
By surveying only respondents with a bachelor's degree, the researcher systematically excludes other segments of the population who may have different opinions about the bond issue. Educational attainment may influence voting behavior, making the sample biased toward a particular viewpoint. As a result, the findings cannot be generalized to the entire voting population.
While the wording of the question may be persuasive, the primary statistical error is the **non-random and restricted selection of respondents**. Response bias relates to how participants answer questions, whereas this issue arises before responses are even collected. Faulty operationalization and confusion of causality are not applicable here.
Data-driven decision making stresses ethical sampling practices to avoid misleading conclusions. Therefore, the correct answer is **D**, selection bias.
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NEW QUESTION # 78
What is true about outliers?
Choose 2 answers.
Answer: C,D
Explanation:
Outliers are observations that differ substantially from the rest of the data, but they are not automatically errors and should not be removed without investigation. One correct statement is that miskeyed outliers can be corrected before analysis. For example, if a value of 500 is entered instead of 50, this is a data-entry error, not a meaningful observation, and it should be fixed using source verification. Another correct statement is that outliers can help determine whether something does not belong in the study. They may reveal invalid records, unusual conditions, a different population, process breakdowns, or rare but important events. The incorrect choices are too absolute. Not all outliers are statistically significant, even under a normal distribution, because significance depends on context, sample size, and method. Likewise, not all observed outliers should be eliminated. Some outliers contain valuable information and can indicate real variation that deserves attention. In quality control, fraud detection, medical screening, and operational monitoring, outliers may be among the most important data points. Therefore, the correct answers are the ones that recognize both correction of miskeyed values and the analytical value of identifying unusual observations.
NEW QUESTION # 79
What was the cumulative incidence rate during Year 2 at the university?
Answer: C
Explanation:
Cumulative incidence is the proportion of a population initially at risk that develops a condition during a specified period. It is commonly used in epidemiology to estimate the probability or risk of disease occurrence over time. In this question, the correct Year 2 cumulative incidence rate is 11.84 percent. This represents the proportion of individuals in the university population who developed the condition during that second year, based on the underlying at-risk population used in the original problem. Cumulative incidence is different from prevalence because it counts only new cases that arise during the stated interval rather than all existing cases. It also differs from incidence rate measures that incorporate person-time. Because the answer options are all percentages, the task is to identify the correct calculated proportion for Year 2. Based on the provided choices, 11.84 percent is the correct cumulative incidence value. Therefore, the correct answer is
11.84 percent.
NEW QUESTION # 80
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