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| Section | Weight | Objectives |
|---|---|---|
| Probability & Statistical Inference | 20% | - Correlation vs. causation - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals |
| Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression - Forecasting & trend analysis |
| Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value |
| Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions |
| Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution |
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NEW QUESTION # 90
Which tool sorts data into categories to help teams identify the most significant factors that contribute the most to problems?
Answer: A
Explanation:
APareto chartsorts data into categories and ranks them by frequency or impact. In data-driven decision making, this helps teams focus on the most significant contributors to a problem.
The chart combines bars and a cumulative line to highlight which factors account for the largest share of issues. This aligns with the Pareto principle and supports prioritization of improvement efforts.
Run charts track data over time, flowcharts describe processes, and cause charts are not a standard quality tool. Therefore, the correct answer isC.
NEW QUESTION # 91
Which two tools make it easier to detect an out-of-range error?
Choose 2 answers.
Answer: A,D
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 # 92
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: A,C
Explanation:
Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is that more time is spent managing data than analyzing data. Analysts must devote substantial effort to cleaning, validating, and correcting errors before meaningful analysis can occur, delaying insights and increasing costs.
Another critical result is that missing data tend to skew the results of the analysis. Incomplete data can distort averages, trends, and statistical relationships, leading to biased conclusions and unreliable decisions. This is especially problematic in predictive and inferential analytics, where assumptions about data completeness are essential.
Using spreadsheets or placing data in charts does not inherently result from flawed data, nor does it resolve data quality issues. While visualization can help identify errors, it is not a direct outcome of starting with flawed data.
Data-driven decision making emphasizes that poor-quality input leads to poor-quality output. Ensuring data accuracy and completeness before analysis is essential for producing valid insights. Therefore, the correct answers are B and D.
NEW QUESTION # 93
Which use of statistics would apply to employees?
Answer: B
Explanation:
Statistics can be applied to employees and organizations in many ways, but among the choices given, predicting future levels of financial risk is the best fit for a practical statistical use. Organizations often use statistical models to evaluate uncertainty related to staffing, benefits, payroll obligations, productivity changes, turnover, insurance exposure, and broader business performance. These analyses help managers make more informed decisions about budgeting, hiring, workforce planning, and operational resilience. The other options are less directly tied to employee-related statistical application. Influencing vendor prices and comparing wholesale pricing are more related to procurement and market analysis than to employees.
Determining financial interest rates generally falls under financial markets, lending, or macroeconomic policy rather than an employee-centered use of statistics. In a data-driven environment, statistical tools are frequently used to forecast risk and evaluate future scenarios so that organizations can protect resources and plan responsibly. Therefore, predicting future levels of financial risk is the most accurate answer because it reflects a recognized analytical application of statistics within organizational decision-making.
NEW QUESTION # 94
Why is quantitative analysis important to the decision-making process?
Answer: D
Explanation:
Quantitative analysis is important in decision-making because it focuses on the systematic examination of measurable data, allowing organizations to evaluate situations objectively rather than relying only on intuition or personal judgment. A key strength of quantitative analysis is that it can examine and describe large sets of data in ways that reveal patterns, trends, relationships, and performance outcomes. This makes it especially useful in business, operations, finance, healthcare, and policy environments where decisions must be supported by evidence. By converting information into numbers, decision-makers can compare alternatives, estimate likely outcomes, and justify their choices with observable facts. While dashboards and surveys may support the process, they are not the fundamental reason quantitative analysis matters. Its real value lies in its ability to transform raw data into meaningful insights that improve planning, forecasting, risk evaluation, and resource allocation. Therefore, the best answer is the choice that identifies its central purpose: examining and describing large sets of data in a clear, measurable way.
NEW QUESTION # 95
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