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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis - Simple & multiple linear regression |
| Topic 2: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Correlation vs. causation - Hypothesis testing, p-values, confidence intervals |
| Topic 3: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance - Role of data in business decisions |
| Topic 4: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
| Topic 5: Statistical Concepts & Descriptive Analytics | 25% | - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods |
>> Reliable Data-Driven-Decision-Making Test Objectives <<
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NEW QUESTION # 12
Which element of an experimental study is described as the procedures applied to each subject?
Answer: B
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 # 13
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 # 14
A sample of 1,020 people was asked how many minutes they exercise on a typical day. The data were plotted and determined to beskewed leftwith a mean of 44.75 minutes.
Which boxplot correctly graphs this data?
Answer: B
NEW QUESTION # 15
Which statement is an accurate reflection of an analytical decision made by a for-profit organization?
Answer: B
Explanation:
A for-profit organization typically uses analytical methods to improve outcomes such as revenue, efficiency, market share, and especially profit. Among the available choices, linear programming is the recognized analytical optimization technique used to maximize or minimize an objective function subject to constraints.
In business settings, firms may use linear programming to optimize product mix, staffing, shipping routes, inventory allocation, advertising budgets, or production schedules. The purpose is to find the best possible decision based on limited resources such as time, labor, capital, or materials. The other options do not reflect standard analytical decision-making terminology in data-driven management. "Software programming" and
"hardware programming" refer to technology development activities, not profit-optimization methods, and
"social programming" does not describe a formal business optimization tool. Because the question asks for an accurate reflection of an analytical decision in a for-profit context, the correct answer is the one that uses a recognized prescriptive analytics method to optimize profit. That method is linear programming, which is widely used in operations research and managerial decision-making.
NEW QUESTION # 16
An analyst used multiple linear regression to show how a big box store's sales (y) are predicted by the big box store's advertising expenditure in dollars (variable x1) and the advertising expenditure in dollars of a specialty store (variable x2) in the same market. The estimated regression is y = 651.57 + 92.30x1 # 26.89x2. How are advertising expenditures and sales related in this scenario?
Answer: D
NEW QUESTION # 17
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