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| Section | Objectives |
|---|---|
| Hypothesis Testing | - Null and alternative hypotheses - t-tests, chi-square tests, and significance testing |
| Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
| Decision Making Models | - Decision trees and expected value analysis - Risk and uncertainty in decision-making |
| Data Fundamentals and Business Analytics | - Data types and data collection methods - Descriptive statistics (mean, median, variance, standard deviation) |
| Probability and Statistical Inference | - Probability concepts and distributions - Sampling methods and sampling error |
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NEW QUESTION # 115
Which performance metric simultaneously accounts for financial, customer, internal process, and learning metrics?
Answer: B
Explanation:
The balanced scorecard is the performance framework that simultaneously accounts for financial, customer, internal process, and learning and growth metrics. It was developed to provide a more complete view of organizational performance than financial measures alone. By incorporating these four perspectives, organizations can connect day-to-day activities with long-term strategy and ensure that performance is evaluated in a balanced way. Financial measures show economic results, customer measures reflect market and service outcomes, internal process measures track operational efficiency and quality, and learning metrics focus on improvement, capability development, and organizational growth. A customer complaint report addresses only one narrow area. A balance sheet and an income statement are financial documents and do not capture the full multidimensional view described in the question. Because the question asks for the metric that integrates all four of these important performance areas, the correct answer is balanced scorecard.
NEW QUESTION # 116
What must be analyzed using powerful analytic tools?
Answer: C
Explanation:
Big datamust be analyzed using powerful analytic tools due to its volume, velocity, and variety. In data- driven decision making, traditional tools are often insufficient for processing massive, complex datasets generated from digital platforms, sensors, and transactions.
Big data requires advanced computing power, specialized software, and sophisticated algorithms to extract meaningful insights. Inferential statistics and small datasets can often be handled with conventional statistical tools.
Therefore, the correct answer isC, big data.
NEW QUESTION # 117
What is the purpose of the quality management principle of dedication to fact-based decision-making?
Answer: A
Explanation:
The principle offact-based decision-makingemphasizes using reliable data and objective analysis rather than intuition or opinion. In data-driven decision making, this principle exists primarily toreduce bias and increase trust in organizational plans and decisions.
When decisions are grounded in verified data, assumptions are challenged, personal biases are minimized, and outcomes are more predictable. This builds confidence among stakeholders and supports transparency and accountability.
Customer loyalty, waste elimination, and quality effectiveness may be indirect benefits, but the core purpose is ensuring that decisions are objective, defensible, and evidence-based. Therefore, the correct answer isD.
NEW QUESTION # 118
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: B,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 # 119
Which tool should be used to closely monitor inputs and outputs?
Answer: C
Explanation:
ASIPOC diagram(Suppliers, Inputs, Process, Outputs, Customers) is specifically designed to closely monitor and understand the flow of inputs and outputs within a process. In data-driven decision making and quality management, SIPOC diagrams provide a high-level view of how value is created and delivered.
By clearly identifying suppliers and inputs at the start of a process and outputs and customers at the end, organizations can assess whether inputs meet requirements and whether outputs align with customer expectations. This visibility helps identify inefficiencies, gaps, or quality issues early in the process lifecycle.
Business process diagrams focus on workflow steps but do not emphasize supplier-input and output- customer relationships. Financial statements and pro forma statements are financial planning tools and are not designed for operational process monitoring.
Therefore, the correct answer isC, SIPOC diagram.
NEW QUESTION # 120
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