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| Section | Weight | Objectives |
|---|---|---|
| Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value - Quality & process improvement tools |
| Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression - Forecasting & trend analysis |
| Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions - Ethics, privacy, and data governance |
| Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value - Correlation vs. causation |
| Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution |
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NEW QUESTION # 71
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: B
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 # 72
Which two characteristics must a researcher consider concerning data quality when ensuring that an analysis is based on a clean data set?
Choose 2 answers.
Answer: A,C
Explanation:
When evaluating whether a data set is clean enough for analysis, a researcher must focus on data quality dimensions that directly affect validity and usefulness. Two important characteristics are uniqueness and relevance. Data elements must be unique to prevent duplicate records from distorting counts, averages, totals, and trend analyses. Duplicate entries can lead to biased results, especially in customer, transaction, or survey data. Relevance is equally important because even accurate data are not helpful if they do not pertain to the question being studied. A clean data set should support the actual purpose of the analysis rather than merely being complete or large. The statement about age is incorrect because timeliness often matters; outdated data may no longer reflect the current environment. The statement that data cannot contain outliers is also too absolute. Outliers may be valid observations and can sometimes reveal important conditions, anomalies, or data-entry problems that require investigation rather than automatic removal. Thus, the best two characteristics are uniqueness and relevance, because both directly support meaningful, accurate, and decision- ready analysis.
NEW QUESTION # 73
Which performance metric simultaneously accounts for financial, customer, internal process, and learning metrics?
Answer: A
Explanation:
Thebalanced scorecard (BSC)is a performance management framework that simultaneously accounts for financial, customer, internal process, and learning and growth metrics. In data-driven decision making, the balanced scorecard provides a holistic view of organizational performance rather than focusing on a single dimension of success.
Financial metrics assess profitability and sustainability, customer metrics evaluate satisfaction and loyalty, internal process metrics examine operational efficiency, and learning and growth metrics focus on employee development and innovation. By integrating these perspectives, the balanced scorecard ensures alignment between day-to-day operations and long-term strategic goals.
Customer complaint reports, income statements, and balance sheets each address only one aspect of performance. They do not provide the multi-dimensional insight necessary for strategic decision-making.
Therefore, the correct answer isA, balanced scorecard.
NEW QUESTION # 74
A digital marketing manager wants to determine whether conversion rates from the company's latest email campaign are about the same as the industry average or significantly different. Which statistical concept should be used to measure the data set?
Answer: C
Explanation:
To determine whether the campaign's conversion rates are about the same as the industry average or significantly different, the manager must consider not only the average value but also how much variation exists in the data. Standard deviation is the measure that captures the spread or variability of the data around the mean. This makes it essential when evaluating whether an observed conversion rate is unusually high, unusually low, or within a normal expected range compared with the industry. The mean gives the central average, but by itself it does not show whether a result is significantly different. The median identifies the middle value, which is useful in skewed data but not sufficient for judging statistical difference in this context. A bell curve describes the shape of a normal distribution rather than serving as the core numerical measure needed here. Since the question is about determining whether results differ meaningfully from an average benchmark, standard deviation is the most appropriate concept among the options provided.
NEW QUESTION # 75
Which element of an experimental study is described as the procedures applied to each subject?
Answer: A
Explanation:
In an experimental study,treatmentsare defined as the specific procedures or conditions applied to each subject or experimental unit. This is a fundamental concept in experimental design within data-driven decision making and inferential statistics.
Experimental units are the subjects or entities being studied, such as individuals, machines, or products.
Responses are the measured outcomes observed after the treatment is applied. Inputs are factors or variables that may influence the experiment but are not the procedures themselves. Treatments, however, represent the deliberate interventions introduced by the researcher to study their effect on the response variable.
For example, in a pricing experiment, different price levels applied to customers would be considered treatments. In a manufacturing experiment, different machine settings would serve as treatments. By systematically varying treatments, analysts can determine causal relationships between variables.
Data-driven decision making relies on well-designed experiments to support valid conclusions. Clearly defining treatments ensures that the effects of specific actions can be isolated, measured, and analyzed accurately. Therefore, the correct answer isC, as treatments describe the procedures applied to each subject.
NEW QUESTION # 76
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