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WGU Data-Driven-Decision-Making Exam Syllabus Topics:

SectionObjectives
Topic 1: Probability and Statistical Inference- Sampling methods and sampling error
- Probability concepts and distributions
Topic 2: Hypothesis Testing- t-tests, chi-square tests, and significance testing
- Null and alternative hypotheses
Topic 3: Regression and Correlation Analysis- Interpreting correlation and causation
- Linear regression modeling
Topic 4: Data Fundamentals and Business Analytics- Descriptive statistics (mean, median, variance, standard deviation)
- Data types and data collection methods
Topic 5: Decision Making Models- Decision trees and expected value analysis
- Risk and uncertainty in decision-making

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WGU VPC2Data-Driven Decision MakingC207 Sample Questions (Q100-Q105):

NEW QUESTION # 100
Which type of study is also known as a quasi-experimental study?

Answer: C

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 # 101
A school board has nine voting members. Five members need to be chosen each year for the finance committee. Why should the combination technique be used when selecting committee members?

Answer: A

Explanation:
The combination technique is appropriate when selecting items or people from a group and the order of selection does not matter. In this case, five members are being chosen from nine voting members to serve on the finance committee. What matters is which five individuals are selected, not the sequence in which their names are chosen. Combinations are used precisely for this type of situation because they count unique groups without regard to arrangement. If order did matter, then a permutation approach would be required instead. The options referring to correlation and meeting attendance are unrelated to the mathematical reason for using combinations. This is a straightforward committee-selection problem, which is one of the most common applications of combinations in probability and counting methods. Recognizing whether order matters is the essential step in choosing between combinations and permutations. Since a finance committee is defined by its membership rather than by the order in which members were picked, the correct answer is that the selection order of committee members is not important.


NEW QUESTION # 102
A laboratory uses a graduated cylinder to measure liquid volume in metric units. Which type of experimental design is this laboratory implementing?

Answer: C

Explanation:
A laboratory measuring liquid volume with a graduated cylinder in metric units is using quantitative research because the process involves numerical measurement and objective data collection. Quantitative research is concerned with collecting measurable data that can be analyzed statistically or compared using mathematical methods. In this case, the volume of liquid is expressed in standard units such as milliliters or liters, making it a clear numerical variable. Qualitative research, by contrast, focuses on descriptions, experiences, opinions, or non-numeric observations. "Development research" and "precision research" are not the best classifications for the method described in the question. The central feature here is that the laboratory is collecting measurable, numerical data using an instrument designed for exact observation. This aligns directly with the principles of quantitative analysis, where standardization, objectivity, and repeatability are essential. Such measurement-based methods are foundational in scientific experiments, industrial testing, and evidence-based decision-making because they allow researchers to compare results consistently and apply statistical reasoning to their findings.


NEW QUESTION # 103
What is a basic assumption of a z-score?

Answer: B

Explanation:
Az-scorestandardizes a value by expressing how many standard deviations it lies from the mean. A fundamental assumption of z-score analysis in data-driven decision making is that the data can be transformed to astandard normal distributionwith amean of zero and a standard deviation of one.
This transformation allows analysts to compare values from different distributions on a common scale and to calculate probabilities using the standard normal table. The formula for a z-score subtracts the mean from the observed value and divides by the standard deviation, resulting in this standardized distribution.
Outliers are not eliminated by default in z-score calculations; instead, z-scores are often used to identify outliers. A standard deviation of 2 is incorrect and would not represent a standardized distribution.
Therefore, the correct answer isA, reflecting the core assumption underlying z-score usage.


NEW QUESTION # 104
Which graphical display is used to examine the distribution of a data set with quartiles?

Answer: A

Explanation:
Aboxplotis specifically designed to display the distribution of a dataset using quartiles. In data-driven decision making, boxplots visually summarize data through the minimum, first quartile, median, third quartile, and maximum.
Boxplots are useful for identifying spread, central tendency, skewness, and potential outliers. Scatterplots and bivariate charts analyze relationships between variables, while Pareto charts rank categorical data by frequency.
Because quartiles are the defining feature of a boxplot, the correct answer isC.


NEW QUESTION # 105
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