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| Section | Objectives |
|---|---|
| Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
| Data Fundamentals and Business Analytics | - Data types and data collection methods - Descriptive statistics (mean, median, variance, standard deviation) |
| Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
| Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Decision Making Models | - Decision trees and expected value analysis - Risk and uncertainty in decision-making |
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NEW QUESTION # 61
Which type of study is also known as a quasi-experimental study?
Answer: A
Explanation:
A **quasi-experimental study** is commonly referred to as an **observational study** in data-driven decision making. Unlike true experiments, quasi-experimental studies do not involve random assignment of subjects to treatment and control groups. Instead, researchers observe outcomes in naturally occurring groups and attempt to draw conclusions about relationships between variables.
In observational studies, the researcher does not control the assignment of treatments. As a result, these studies are more susceptible to bias and confounding variables than randomized experiments. However, they are often necessary when controlled experimentation is impractical, unethical, or too costly. For example, studying the impact of policy changes or economic conditions typically relies on observational data.
Blind studies are a form of experimental design used to reduce bias, hypothesis testing is a statistical process rather than a study type, and content validity refers to measurement quality. None of these represent quasi- experimental designs.
In data-driven decision making, observational (quasi-experimental) studies are valuable for identifying associations and generating insights, but analysts must be cautious not to infer causality without proper controls. Therefore, the correct answer is **A**.
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NEW QUESTION # 62
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?
Answer: A
Explanation:
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.
NEW QUESTION # 63
A manager has been assigned to manage a digital marketing analytics team. The manager tasks the team with determining similarities among existing customers in the company's database, such as similarities in products purchased, location, and the average amount spent per order among existing customers.
Which type of activity will help the team accomplish this task?
Answer: D
Explanation:
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.
NEW QUESTION # 64
What is the 80/20 rule associated with a Pareto chart?
Answer: D
Explanation:
The80/20 rule, or Pareto principle, states that80% of problems result from 20% of causes. In data-driven decision making, this concept helps organizations prioritize efforts where they will have the greatest impact.
Pareto charts visually demonstrate this principle by showing that a small number of factors contribute disproportionately to overall problems. By addressing these critical few causes, organizations can achieve significant improvements with focused effort.
Therefore, the correct answer isD.
NEW QUESTION # 65
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 # 66
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