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| Section | Objectives |
|---|---|
| Data Analysis Fundamentals | - Data Visualization Techniques - Data Types and Measurement Scales - Data Quality and Cleaning - Descriptive Statistics |
| Decision-Making Frameworks | - Evidence-Based Decision Making - Cost-Benefit Analysis - Rational Decision Models - Risk Analysis and Assessment |
| Data-Driven Culture and Communication | - Stakeholder Communication - Presenting Data Insights - Building Data-Informed Organizations - Ethical Considerations in Data Use |
| Business Intelligence and Analytics | - Dashboards and Reporting - Predictive Analytics Basics - Key Performance Indicators (KPIs) - Data Mining Concepts |
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NEW QUESTION # 44
Research data indicate 95% confidence in a study in which subjects who were shown a product advertisement exhibited brand awareness compared to a control group who did not see the advertisement.
What can be concluded from this study?
Answer: A
NEW QUESTION # 45
What is true about outliers?
Choose 2 answers.
Answer: B,D
Explanation:
Outliers are observations that differ substantially from the rest of the data, but they are not automatically errors and should not be removed without investigation. One correct statement is that miskeyed outliers can be corrected before analysis. For example, if a value of 500 is entered instead of 50, this is a data-entry error, not a meaningful observation, and it should be fixed using source verification. Another correct statement is that outliers can help determine whether something does not belong in the study. They may reveal invalid records, unusual conditions, a different population, process breakdowns, or rare but important events. The incorrect choices are too absolute. Not all outliers are statistically significant, even under a normal distribution, because significance depends on context, sample size, and method. Likewise, not all observed outliers should be eliminated. Some outliers contain valuable information and can indicate real variation that deserves attention. In quality control, fraud detection, medical screening, and operational monitoring, outliers may be among the most important data points. Therefore, the correct answers are the ones that recognize both correction of miskeyed values and the analytical value of identifying unusual observations.
NEW QUESTION # 46
What is the formula for calculating a simple index number?
Answer: D
Explanation:
Asimple index numberis used in data-driven decision making to measure relative change in a variable over time, commonly prices, quantities, or values. The standard formula for a simple price index compares the value in the current period to a base period and expresses the change as a percentage.
The correct formula is thecurrent price divided by the base year's price, multiplied by 100. This standardization allows analysts and managers to easily interpret changes relative to a reference point. An index value of 100 indicates no change from the base year, values above 100 indicate an increase, and values below 100 indicate a decrease.
This approach is widely used in economic analysis, inflation tracking, and business trend evaluation because it simplifies comparisons across time. Dividing the base year by the current price would reverse interpretation, and omitting multiplication by 100 would fail to express the index in percentage terms.
Therefore, optionAcorrectly represents the formula for calculating a simple index number.
NEW QUESTION # 47
When researchers are studying the effect of new drug treatments on patients, bias can be introduced by patients if they are aware of who receives the placebo.
Which type of research design eliminates this type of bias?
Answer: B
Explanation:
Ablind studyis specifically designed to eliminate bias that occurs when participants are aware of treatment assignments. In data-driven decision making and experimental research, patient awareness of receiving a placebo or treatment can influence reported symptoms, perceived effectiveness, and behavior, thereby biasing results.
In a blind study, participants do not know whether they are receiving the treatment or the placebo. This prevents expectations or beliefs from influencing outcomes and ensures that observed effects are attributable to the treatment itself rather than psychological or behavioral factors.
Observational studies and prospective cohort studies do not involve controlled assignment of treatments and therefore cannot eliminate this type of bias. Time series studies analyze data over time but do not address participant awareness of treatment allocation.
By preventing patients from knowing their treatment group, blind studies improve internal validity and support more accurate causal inference. Therefore, the correct answer isD, blind study.
NEW QUESTION # 48
Why is choosing appropriate performance indicators important for data-driven decision-making?
Answer: B
Explanation:
Performance indicators guide behavior, priorities, and decision-making. In data-driven decision making, selecting theright indicators ensures alignment with an organization's definition of success. Metrics determine what is measured, monitored, and improved.
If indicators are poorly chosen, organizations may optimize the wrong outcomes, leading to unintended consequences. Well-aligned indicators translate strategic goals into measurable targets and ensure that analytics support meaningful results.
Rules, motivation, and compliance may be secondary effects, but the primary purpose of performance indicators is strategic alignment. Therefore, the correct answer isC.
NEW QUESTION # 49
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