According to the Business Data Analytics (IIBA- CBDA) principles, extreme outliers in a dataset can be identified visually on a scatter plot as points that are distinctly separate from the bulk of the data. In this visual, there are three points that are significantly higher on the y-axis (credit card expense) relative to their position on the x-axis (household income), indicating unusual credit card purchases.

Reference: The identification and interpretation of outliers is a standard practice in data analytics and is covered under the Business Data Analytics (IIBA- CBDA) learning resources.

A scatterplot is a type of chart that shows the relationship between two variables by plotting data points on a two-dimensional plane. A scatterplot can help the professor to understand the normal tendency and outliers of exam performance results over the past 5 years by displaying the distribution, trend, and correlation of the data. For example, the professor can use the x-axis to represent the year and the y-axis to represent the exam score, and see how the scores vary over time and across different exams. Outliers can be identified as data points that are far away from the main cluster or the line of best fit12

Reference: 1: Scatter Plot - Statistics How To 2: Scatterplots - IIBA BABOK Guide v3

The relationship between a Customer entity and an Order entity, where a customer entry will be present in the Customer entity only if they have made an order, is a zero-to-one relationship. This means that for each record in the Order entity, there can be either zero or one record in the Customer entity that is related to it. This implies that the Order entity is optional for the Customer entity, and the Customer entity is mandatory for the Order entity12

Reference: 1: A Guide to the Entity Relationship Diagram (ERD) - Database Star 2: Developing an Application - Oracle

The data scientist is encouraging using the Gestalt Principle of Similarity with regards to data visualization. This principle states that the brain groups objects together that are similar in appearance, such as color, shape, size, or orientation. By using color to group objects needing to be associated, the data scientist is suggesting a way to reduce the complexity in the model and make it easier for the viewers to perceive the patterns and relationships among the data12

Reference: 1: Gestalt Principles For Data Visualization - Topcoder 2: Introduction to Data Visualization: Gestalt Principles

The business analyst is concerned about the validity of the survey. Validity is the extent to which a survey measures what it intends to measure. In this case, the survey is supposed to measure job satisfaction, but the two questions asked to different groups of employees are not equivalent or relevant to this construct. The question ''Do you enjoy working in your workplace?'' is more directly related to job satisfaction than the question ''Do you like the current work benefits?''. The latter question may capture only one aspect of job satisfaction, and may not reflect the overall level of contentment or happiness with the job. Therefore, the survey results may not be valid or accurate in measuring job satisfaction12

Reference: 1: Survey and questionnaires in business analysis - The Functional BA 2: Job Satisfaction Survey - Paul Spector

Explanation: Data governance is the practice of establishing and enforcing policies, standards, roles, and responsibilities for the quality, security, and usage of data across an organization1. Data governance helps ensure that data is consistent, reliable, and trustworthy, and that it aligns with the organization's strategic goals and objectives.Data governance also facilitates collaboration and communication among different stakeholders, such as business analysts, data owners, data stewards, and data consumers2.By implementing data governance, the analyst can influence how the organization strategically plans for business analytics, as data governance can help address the issues of data quality, data integration, data access, data ethics, and data value3. Data integration, data management, and data warehousing are related but distinct concepts from data governance.Data integration is the process of combining data from different sources into a unified view4.Data management is the process of collecting, storing, organizing, and maintaining data throughout its lifecycle5. Data warehousing is the process of creating and maintaining a centralized repository of data for analytical purposes.While these practices can support business analytics, they do not necessarily influence how the organization strategically plans for business analytics, as they are more focused on the technical aspects of data rather than the organizational aspects of data.

Explanation: According to the Guide to Business Data Analytics, a narrative is a way of communicating the results of data analysis in a clear, concise, and compelling manner. A narrative should include the following elements: the purpose of the analysis, the main findings and insights, the implications and recommendations, and the evidence and reasoning. A narrative should also use appropriate language, tone, and style for the intended audience and medium. A narrative can enhance the impact and value of the data analysis by providing context, explanation, and interpretation of the data, as well as by highlighting the key messages and actions. A dashboard that lacks narrative may not be able to convey the full meaning and significance of the data, and may not be able to engage the audience or influence their decision-making.

Explanation: According to the Guide to Business Data Analytics, one of the key competencies of a business data analyst is to identify the research questions that guide the analytics work1.The research questions should be based on the business problem or opportunity, the stakeholder needs, and the data availability and quality2. By providing the focus area of the study, the Operational Manager has limited the scope of the study with their biased opinion, as they have not considered other possible factors that might affect the operational efficiencies, such as demand, inventory, quality, labor, or customer satisfaction. The Operational Manager has also not involved other stakeholders who might have different perspectives or interests in the study. This could lead to a narrow or incomplete analysis that might miss some important insights or recommendations.The Operational Manager should instead collaborate with the analyst to define the research questions that are relevant, specific, measurable, achievable, and time-bound3. The other options are not correct, as they do not address the issue of defining the research questions. The Operational Manager is not necessarily the expert on the operational processes, as they might have a limited or biased view of the situation. The Operational Manager has not limited the scope of the budget by providing a timeline of three weeks, as this is a reasonable time frame for an analytics study, depending on the complexity and availability of the dat a.The Operational Manager has not helped the analyst stay on track with time and budget by providing the focus areas, as this might actually waste time and resources if the focus areas are not aligned with the actual business problem or opportunity.

A/B testing, also known as randomized controlled trial or split testing, is a technique that compares the outcomes of two or more groups that are randomly assigned to different treatments or interventions. The purpose of A/B testing is to measure the causal effect of the treatments on the outcomes of interest, such as pain reduction in this case. A/B testing can help determine whether the new medicine is more effective than the existing drug by comparing the average pain scores of the two groups of participants after the treatment.