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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
| 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% | - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards |
| Topic 4: Probability & Statistical Inference | 20% | - Correlation vs. causation - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals |
| Topic 5: Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis |
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NEW QUESTION # 108
An organization develops a new strategic plan and seeks ways to improve process performance by reducing variation to only 3.4 defects per million process outputs.
Which tool can the organization use to meet this goal?
Answer: A
Explanation:
Six Sigmais specifically designed to reduce process variation to3.4 defects per million opportunities, making it the correct tool for achieving this goal. In data-driven decision making, Six Sigma provides a structured methodology (DMAIC) for defining problems, measuring performance, analyzing root causes, improving processes, and controlling future outcomes.
Statistical process control supports monitoring, but Six Sigma integrates SPC with strategic improvement initiatives. Linear programming optimizes resource allocation, and just-in-time focuses on inventory efficiency rather than defect reduction.
Therefore, the correct answer isD, Six Sigma.
NEW QUESTION # 109
A laboratory uses a graduated cylinder to measure liquid volume in metric units. Which type of experimental design is this laboratory implementing?
Answer: B
Explanation:
A laboratory measuring liquid volume with a graduated cylinder in metric units is using quantitative research because the process involves numerical measurement and objective data collection. Quantitative research is concerned with collecting measurable data that can be analyzed statistically or compared using mathematical methods. In this case, the volume of liquid is expressed in standard units such as milliliters or liters, making it a clear numerical variable. Qualitative research, by contrast, focuses on descriptions, experiences, opinions, or non-numeric observations. "Development research" and "precision research" are not the best classifications for the method described in the question. The central feature here is that the laboratory is collecting measurable, numerical data using an instrument designed for exact observation. This aligns directly with the principles of quantitative analysis, where standardization, objectivity, and repeatability are essential. Such measurement-based methods are foundational in scientific experiments, industrial testing, and evidence-based decision-making because they allow researchers to compare results consistently and apply statistical reasoning to their findings.
NEW QUESTION # 110
A county government wants to start a cost-effective recycling program. How can the county apply data analytic approaches to attain this goal?
Answer: C
Explanation:
To create a cost-effective recycling program, the county should benchmark similar strategies used by other counties. Benchmarking is a practical data-driven approach that allows decision-makers to compare program designs, costs, participation rates, collection methods, and outcomes across jurisdictions facing similar conditions. By examining how other counties structure successful recycling initiatives, the county can identify efficient practices, realistic cost expectations, and implementation strategies that have already been tested in comparable environments. This approach is especially useful when developing a new program because it reduces trial-and-error and supports evidence-based planning. The other options are less directly related to designing and evaluating a recycling program. Analyzing county contracts or aligning funds with staffing levels may be useful in general budgeting, but they do not specifically address recycling strategy effectiveness. Audit performance research is also not closely tied to recycling program design. Therefore, the most appropriate data analytic approach is benchmarking similar strategies of other counties.
NEW QUESTION # 111
A bakery owner would like to know how many cakes to sell for monthly profit to equal zero. Which analysis method should the owner perform?
Answer: C
Explanation:
The bakery owner wants to determine the sales level at which profit equals zero. This is the definition of break-even analysis. Break-even analysis identifies the number of units that must be sold so that total revenue exactly equals total cost, meaning there is neither profit nor loss. It is a widely used prescriptive and managerial decision tool for pricing, budgeting, production planning, and cost control. ANOVA is used to compare means across groups, not to find a zero-profit sales level. A t-test compares means between two groups, which is also unrelated to the goal of determining the required sales quantity for no profit or loss.
"Crossover" is not the standard term for this type of profitability calculation in business analytics. Break-even analysis helps managers understand fixed costs, variable costs, contribution margin, and the minimum output required to sustain operations. Therefore, the correct method for determining how many cakes must be sold so that monthly profit equals zero is break-even analysis.
NEW QUESTION # 112
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: A
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 # 113
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