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| Section | Objectives |
|---|---|
| Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
| Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
| Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
| Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
>> Data-Driven-Decision-Making試験過去問 <<
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質問 # 22
Which two tools make it easier to detect an out-of-range error?
Choose 2 answers.
正解：A、D
解説：
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.
質問 # 23
What describes fact-based decision-making according to quality management principles?
Choose 2 answers.
正解：B、D
解説：
Fact-based decision-making is a central quality management principle because it encourages organizations to rely on evidence, measurement, and analysis rather than assumptions or intuition alone. Decisions foster trust in plans when stakeholders can see that choices are grounded in objective information and sound reasoning.
This strengthens accountability and confidence in management actions. Fact-based decisions also help reduce external bias because they rely on verified information rather than opinions, pressure, or unsupported judgment. In contrast, decisions based only on the instincts of experienced leaders do not reflect the core meaning of fact-based management, even though experience may still be valuable. The statement about supplier relationships is not a defining description of fact-based decision-making and does not capture the principle itself. Quality management emphasizes using reliable data to guide planning, improve processes, and support consistent outcomes. Therefore, the two correct answers are that such decisions foster trust in plans and reduce external bias.
質問 # 24
A financial analyst theorizes that commute times increase as the percentage of land availability for homes in a city decreases. To test this hypothesis, the analyst uses a regression analysis to explore how land availability predicts commute time.
What does land availability represent in this regression?
正解：B
解説：
In regression analysis, theindependent variableis the predictor used to explain or estimate changes in another variable. In data-driven decision making, identifying the correct variable roles is essential for meaningful interpretation.
In this scenario, land availability is used to predict commute time. Commute time is the outcome being explained, making it the dependent or target variable. Land availability influences or explains changes in commute time, which makes it theindependent variable.
Controls are additional variables included to isolate effects, but land availability is the primary predictor of interest. Therefore, optionCis correct.
質問 # 25
What results from starting an analysis with flawed data?
Choose 2 answers.
正解：C、D
解説：
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.
質問 # 26
A boutique specializing in gifts reviews its sales data over the last year. It observes a slow decline in revenue in the first quarter, a growth in revenue in the second quarter, a slight decline in revenue in the third quarter, and a rapid increase in revenue in the fourth quarter.
Which data pattern type can the sales data be assessed against?
正解：B
解説：
Seasonalityrefers to predictable patterns in data that repeat at regular intervals, such as quarters or months, due to seasonal factors. In data-driven decision making, identifying seasonal patterns helps organizations forecast demand and plan operations.
The boutique's revenue shows distinct quarterly patterns: declines and increases that align with different times of the year. The sharp increase in the fourth quarter is especially indicative of seasonal effects, such as holiday shopping.
Random variation and irregularity describe unpredictable fluctuations, while cyclicality refers to long-term economic cycles rather than recurring annual patterns. Therefore, the correct answer isC, seasonality.
質問 # 27
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