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| Section | Objectives |
|---|---|
| Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
| Regression and Correlation Analysis | - Linear regression modeling - Interpreting correlation and causation |
| Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
| Hypothesis Testing | - Null and alternative hypotheses - t-tests, chi-square tests, and significance testing |
| Data Fundamentals and Business Analytics | - Data types and data collection methods - Descriptive statistics (mean, median, variance, standard deviation) |
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NEW QUESTION # 70
Phone calls for a company are routed randomly to one of eight call centers. Six are based in the United States, and two are based in another country. What is the probability that an incoming call will be routed to a U.S.- based call center?
Answer: B
Explanation:
Probability is calculated as the number of favorable outcomes divided by the total number of possible outcomes, assuming each outcome is equally likely. In this case, there are eight call centers total, and six of them are located in the United States. Since calls are routed randomly, each call center has an equal chance of receiving an incoming call. Therefore, the probability that a call is routed to a U.S.-based call center is 6 out of 8. This fraction simplifies to 3 out of 4, which is equal to 0.75 or 75 percent. The answer choices 25 percent and 33 percent are too small because they do not match the proportion of U.S. call centers. The option 67 percent is closer but still incorrect, as 6 divided by 8 is not 0.67. This is a basic probability problem involving equally likely outcomes. Because six of the eight centers are in the United States, the correct probability is 75 percent.
NEW QUESTION # 71
What are two qualities of key performance indicators (KPIs)?
Choose 2 answers.
Answer: C,D
Explanation:
Key performance indicators (KPIs) are designed to measure progress toward strategic objectives. In data- driven decision making, effective KPIs arestable, meaningful, and aligned with organizational goalsrather than frequently changing.
One important quality of KPIs is that theycan be used for internal benchmarking, allowing organizations to compare performance across departments, time periods, or projects. This helps identify best practices and performance gaps.
Another defining characteristic is that KPIs often followSMART criteria-Specific, Measurable, Achievable, Relevant, and Time-bound. These criteria ensure KPIs are clearly defined and actionable.
KPIs are not meant to be easily changed, as frequent changes undermine consistency and comparability. They also require ongoing monitoring and maintenance to remain relevant and accurate.
Therefore, the correct answers areB and D.
NEW QUESTION # 72
A company runs a regression analysis to identify the impact of volume on demand, which can be shown as an equation y = 50x + 10. Which volume is needed to produce a demand of 10,510?
Answer: C
Explanation:
To solve this regression problem, substitute the given demand value into the equation and solve for x. The equation is y = 50x + 10, and the desired demand is 10,510. Replacing y with 10,510 gives 10,510 = 50x + 10.
Subtracting 10 from both sides gives 10,500 = 50x. Dividing both sides by 50 gives x = 210. This means a volume of 210 is required to produce a demand of 10,510 under the linear relationship described by the equation. Regression equations are often used in forecasting and planning because they allow analysts to estimate one value from another. In this case, the independent variable is volume and the dependent variable is demand. The correct value must satisfy the equation exactly, and only 210 does so. The other options do not produce the required result when substituted into the equation. Therefore, the correct answer is 210.
NEW QUESTION # 73
A student with a degree is presumed to already have a bachelor's degree. Which type of data does this represent?
Answer: A
Explanation:
This question refers to categorizing information rather than measuring it numerically. The phrase "a student with a degree" identifies a classification or label, not a value with mathematical meaning. Nominal data are used to place observations into distinct categories without any inherent numerical order or ranking. In this case, the student is being grouped according to degree status, which is a named category. Interval and ratio data are numerical measurement scales, so they do not apply here. Ordinal data involve ranked categories, such as low, medium, and high, or freshman through senior, where order matters. Here, the information does not describe rank or position; it simply identifies a class of person based on a characteristic. Even though the phrase mentions a bachelor's degree, the key issue is that the information is categorical rather than numeric.
Therefore, this is best understood as nominal data. In data analysis, recognizing nominal variables is important because they are usually summarized with counts, percentages, or category-based comparisons rather than means or standard deviations.
NEW QUESTION # 74
What classifies analytics as descriptive, predictive, or prescriptive?
Answer: B
Explanation:
Analytics is classified as descriptive, predictive, or prescriptive based onthe purpose of the analysis and the methods used to carry it out, which is a foundational concept in data-driven decision making. The distinction reflects the type of managerial question being addressed rather than technical aspects such as software tools, sample size, or data reliability.
Descriptive analytics focuses on understandingwhat has happenedby summarizing historical data. It relies on descriptive statistics, reports, dashboards, and data visualizations to provide insights into past performance.
Predictive analytics extends this approach to determinewhat is likely to happenby using statistical models, probability distributions, regression analysis, and forecasting techniques to estimate future outcomes.
Prescriptive analytics goes further by identifyingwhat should be doneto achieve desired results. It uses optimization models, decision trees, simulations, and scenario analysis to recommend the best course of action under given constraints.
In data-driven decision making, the classification of analytics depends on how results are intended to support decisions and the analytical techniques applied to achieve that goal. Factors such as data quality and software influence accuracy and efficiency but do not define the analytics category itself. Therefore, the correct classification criterion is thepurpose and methods, making optionCthe correct answer.
NEW QUESTION # 75
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