Pass the WGU Courses and Certificates Data-Driven-Decision-Making Questions and answers with ExamsMirror

Quality controlinvolves monitoring, measuring, and analyzing production output to identify defects and variations. In data-driven decision making, quality control focuses on detecting problems after or during production rather than preventing them in advance.

In this scenario, the manager reviews defect data and identifies a pattern indicating more errors during the night shift. This analysis is a classic quality control activity, as it involves examining performance data to detect issues in the production process.

Quality assurance focuses on process design and prevention, while service and underlying activities are not standard quality management classifications. Therefore, the correct answer isD, quality control activity.

When the null hypothesis is accepted in anF-test, it indicates that there is no statistically significant difference between group variances or means, depending on the test design. Acceptance occurs when thetest statistic is less than the critical value, meaning the observed variation is within expected limits.

Accepting the null hypothesis implies thatno meaningful differenceexists between the samples. If the test statistic exceeded the critical value, the null hypothesis would be rejected.

Thus, the correct results areA and D.

Sample size is critical for ensuring **statistical significance** because it determines whether results can be confidently generalized to a larger population. In data-driven decision making, larger and appropriately selected samples reduce sampling error and increase the reliability of statistical estimates.

When sample sizes are too small, observed effects may be due to random variation rather than true underlying patterns. Larger samples provide more precise estimates of population parameters and increase the power of hypothesis tests, making it easier to detect meaningful differences or relationships.

While increasing sample size does not eliminate researcher bias, prevent hypothesis misinterpretation, or remove the need for further analysis, it strengthens the validity of conclusions. Statistical significance depends on sample size, effect size, and variability, all of which influence confidence in results.

Therefore, the correct answer is **A**, as adequate sample sizes allow accurate conclusions to be confidently applied to larger populations.

---

Anomission erroroccurs whencrucial data is missingfrom a dataset, which can significantly compromise the quality of analysis and decision-making. In data-driven decision making, omission errors are a serious concern because missing information can lead to biased results, incorrect interpretations, and flawed conclusions.

Omission errors may arise during data collection, data entry, or data integration processes. For example, failing to record customer demographics, transaction values, or time periods can distort descriptive statistics and weaken predictive models. Unlike inaccuracies, which involve incorrect values, omission errors involve the absence of necessary data altogether.

Outliers represent extreme values and are not omission errors. Similarly, failing to review all data is a process issue rather than a data-quality error definition. Inaccurate data refers to incorrect or erroneous values, not missing ones.

Effective data quality management emphasizes identifying and correcting omission errors through validation rules, completeness checks, and data audits. In data-driven decision making, ensuring that all relevant data is captured is essential for producing reliable insights and supporting sound business decisions. Therefore, the correct answer isD, as an omission error occurs when crucial data is missing.

Thecause-and-effect diagram, also known as thefishbone diagramorIshikawa diagram, is used to identify potential causes of a problem. In data-driven decision making, it helps teams systematically explore root causes rather than focusing on symptoms.

The diagram visually organizes possible causes into categories such as people, processes, materials, machines, and environment. Histograms show distributions, flowcharts depict process steps, and scatter diagrams show relationships between variables.

Therefore, the correct answer isC, cause-and-effect diagram.

In linear regression, the equation y = mx + b is used to predict the value of the dependent variable based on the independent variable. In data-driven decision making, this equation represents the estimated relationship between advertising expenditure and sales revenue.

Here,xrepresents advertising spending,mis the slope (2), andbis the intercept ($25,000). Substituting the planned advertising expenditure of $20,000 into the equation gives:

y = 2(20,000) + 25,000

y = 40,000 + 25,000

y = 65,000

This result represents the expected sales revenue based on the regression model. The intercept indicates baseline sales when advertising spend is zero, while the slope shows the increase in sales for each additional dollar spent on advertising.

Therefore, the company should expect to generate$65,000in sales, making optionCthe correct answer.

In results-based management (RBM),activitiesrepresent the actions undertaken to achieve desired outputs and outcomes. In data-driven decision making, activities include training, assessments, and implementation efforts.

Hiring a third-party company to train staff and assess procedures is an action being performed, not the result of that action. Inputs would include funding or resources, outputs would be trained staff or reports, and outcomes would reflect improved surgical performance.

Therefore, the correct answer isD, activities.

Data miningis the appropriate activity for identifying patterns, similarities, and relationships within large datasets. In data-driven decision making, data mining techniques such as clustering and association analysis are commonly used to segment customers based on behavior and characteristics.

The task described involves uncovering hidden patterns across multiple variables, which aligns directly with data mining objectives. Linear programming focuses on optimization, regression predicts outcomes, and touchpoint analysis examines customer interactions rather than similarities.

Therefore, the correct answer isA, data mining.

Ascatter diagramis used to visually examine therelationship between two quantitative variables. In data-driven decision making, scatter diagrams help analysts assess whether variables move together, whether the relationship is positive, negative, or nonexistent, and whether the relationship appears linear or nonlinear.

Each point on a scatter diagram represents a paired observation of two variables, such as advertising spend and sales revenue or hours studied and test scores. Patterns in the plotted points can suggest correlation, which may later be explored using regression analysis. Scatter diagrams are exploratory tools and do not, by themselves, establish causation.

A prioritization matrix ranks options, frequency differences are examined using bar or Pareto charts, and differences in means are evaluated using hypothesis tests such as t-tests or ANOVA. Therefore, the correct application of a scatter diagram is to demonstraterelationships between variables, making optionBcorrect.

Thechi-square statisticis used to compare frequencies of categorical, mutually exclusive outcomes. In data-driven decision making, it is appropriate for analyzing differences between observed counts.

New and used car sales represent mutually exclusive categories, making chi-square the correct choice. Therefore, the correct answer isB.