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| Section | Weight | Objectives |
|---|---|---|
| Statistical Concepts & Descriptive Analytics | 25% | - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards |
| Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Quality & process improvement tools - Decision trees, payoff matrices, expected value |
| Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value - Correlation vs. causation |
| Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression |
| 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 # 14
Which process is considered a statistical process control activity?
Answer: A
Explanation:
Statistical process control involves using statistical methods to monitor, control, and improve production processes. A central purpose of this approach is to determine whether a process is operating within acceptable variation limits. Therefore, determining if the precision of a manufactured product is within a tolerable range is a direct example of statistical process control. This type of activity uses measured data to evaluate consistency, detect abnormal variation, and maintain quality standards. Evaluating consumer complaints may provide useful quality feedback, but it is not the direct statistical monitoring activity described by process control methods. Aligning marketing strategy with manufacturing capabilities is a business planning issue rather than a process control task. Forecasting future consumer demand is a forecasting and analytics activity, not statistical process control. The defining feature of statistical process control is monitoring production behavior using measurable process data and tolerance limits. Therefore, the correct answer is the option that focuses on determining whether the manufactured product's precision remains within an acceptable range.
NEW QUESTION # 15
Which type of analytics classification uses experimental design and optimization to suggest a course of action?
Answer: A
Explanation:
Prescriptive analyticsis the analytics classification that uses experimental design and optimization techniques to suggest a specific course of action. In data-driven decision making, prescriptive analytics represents the most advanced stage of analytics, as it not only predicts outcomes but also recommends decisions that lead to optimal results.
Descriptive analytics summarizes historical data to explain what has already happened, while predictive analytics uses statistical and probabilistic models to estimate what is likely to happen in the future. Diagnostic analytics focuses on understanding why something happened by identifying root causes. In contrast, prescriptive analytics answers the critical question:what should be done.
Prescriptive analytics relies on methods such as optimization models, simulation, decision trees, and experimental design. These techniques evaluate multiple scenarios, constraints, and objectives to identify the best possible action. For example, organizations use prescriptive analytics to optimize pricing, allocate resources efficiently, schedule operations, or determine optimal investment strategies.
Within data-driven decision-making frameworks, prescriptive analytics bridges analysis and action by directly supporting managerial decision-making. It transforms analytical insights into concrete recommendations that can be implemented to improve performance and outcomes. Therefore, the correct answer isC, as prescriptive analytics explicitly uses experimental design and optimization to suggest a course of action.
NEW QUESTION # 16
What is the basic difference between evaluating costs and benefits in the public and private sectors?
Answer: D
Explanation:
The fundamental difference between cost-benefit evaluation in the public and private sectors lies inhow benefits are defined and measured. In data-driven decision making, private-sector projects primarily focus onrevenue generation and profitability, making optionCthe correct distinction.
Private organizations evaluate benefits using measurable financial outcomes such as revenue, profit margins, and return on investment. These metrics provide clear, quantifiable indicators of success. In contrast, public- sector projects often aim to maximizegeneral public welfare, including social, environmental, and economic benefits that are more difficult to quantify monetarily.
Public-sector benefits may include improved public health, safety, education, or trust in government- outcomes that do not translate directly into revenue. Therefore, while costs are measurable in both sectors, benefits differ substantially in nature.
Options A and B are incorrect because public-sector costs are not minimal and public benefits are often difficult to quantify. Option D incorrectly assigns public welfare to private projects. Thus, the correct answer isC.
NEW QUESTION # 17
Which two tools make it easier to detect an out-of-range error?
Choose 2 answers.
Answer: A,B
Explanation:
Out-of-range errors occur when a data value falls outside the allowable or expected limits for a variable.
Examples include a negative age, a score above the maximum possible value, or a date in an impossible format. The tools most useful for identifying such errors are relational databases and spreadsheets. Relational databases often include validation rules, field constraints, data types, and query capabilities that can detect impossible or invalid entries. For example, a database can restrict a field to numeric values within a set range or flag records that violate defined rules. Spreadsheets can also support error detection through conditional formatting, formulas, filters, data validation, and sorting features that make unusual values easier to spot.
Experimental studies and observational studies are research designs, not data-validation tools. They describe how data are collected, not how errors are detected in stored records. Because the question asks specifically for tools that make out-of-range errors easier to detect, the correct choices are the data-handling tools that support validation and review: relational databases and spreadsheets.
NEW QUESTION # 18
A retail manager collected the following sales-receipt totals from the store's cashiers:
$25, $22, $48, $42, $32, $28, $24, $54, $34, $41, $48
What is the median of this sales-receipt data?
Answer: D
Explanation:
Themedianis the middle value of a dataset when the data is arranged in ascending order. It is a key descriptive statistic used in data-driven decision making because it is resistant to extreme values.
First, sort the data in ascending order:
22, 24, 25, 28, 32,34, 41, 42, 48, 48, 54
There are 11 values in total, so the median is the 6th value. The 6th value is$34, making it the median.
The median provides insight into the typical transaction size without being influenced by unusually large receipts. Therefore, the correct answer isB.
NEW QUESTION # 19
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