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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Foundations of Data-Driven Decision Making | 20% | - Role of data in business decisions - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance |
| Topic 2: Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression - Forecasting & trend analysis |
| Topic 3: Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value - Quality & process improvement tools |
| Topic 4: Statistical Concepts & Descriptive Analytics | 25% | - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods |
| Topic 5: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation |
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NEW QUESTION # 33
Which type of study is also known as a quasi-experimental study?
Answer: D
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 # 34
What are random errors caused by?
Answer: D
Explanation:
Random errors are caused by unpredictable fluctuations that occur naturally in measurement, observation, or recording processes. These errors are not consistently in one direction and do not systematically push results higher or lower. Instead, they introduce variability that can make repeated measurements differ slightly even when conditions seem similar. Examples include minor environmental changes, momentary variations in instrument sensitivity, normal human reaction differences, or small observational inconsistencies. Because random errors are unsystematic, they tend to average out over a large number of observations, although they still reduce precision. By contrast, an instrument that needs calibration is more closely associated with systematic error, because it may consistently overstate or understate measurements. Respondents favoring certain outcomes and biased data also reflect systematic forms of bias rather than random variation. In statistics and quality measurement, distinguishing between random error and systematic error is important because each requires a different response. Random error is mainly addressed through repetition, sample size, and statistical controls, whereas systematic error must be corrected at the source. Therefore, the correct cause of random errors is unpredictable fluctuations in readings.
NEW QUESTION # 35
Which statement is an accurate reflection of an analytical decision made by a for-profit organization?
Answer: C
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 # 36
Which tool should be used to closely monitor inputs and outputs?
Answer: D
Explanation:
ASIPOC diagram(Suppliers, Inputs, Process, Outputs, Customers) is specifically designed to closely monitor and understand the flow of inputs and outputs within a process. In data-driven decision making and quality management, SIPOC diagrams provide a high-level view of how value is created and delivered.
By clearly identifying suppliers and inputs at the start of a process and outputs and customers at the end, organizations can assess whether inputs meet requirements and whether outputs align with customer expectations. This visibility helps identify inefficiencies, gaps, or quality issues early in the process lifecycle.
Business process diagrams focus on workflow steps but do not emphasize supplier-input and output- customer relationships. Financial statements and pro forma statements are financial planning tools and are not designed for operational process monitoring.
Therefore, the correct answer isC, SIPOC diagram.
NEW QUESTION # 37
A researcher seeks to pass a bond issue and asks a sample of respondents who have a bachelor's degree if they are voting in favor of the bond because it would be beneficial to the county.
Which type of error does this represent?
Answer: C
Explanation:
This scenario represents **selection bias**, which occurs when a sample is not representative of the population being studied. In data-driven decision making, valid conclusions depend on collecting data from a sample that accurately reflects the broader population.
By surveying only respondents with a bachelor's degree, the researcher systematically excludes other segments of the population who may have different opinions about the bond issue. Educational attainment may influence voting behavior, making the sample biased toward a particular viewpoint. As a result, the findings cannot be generalized to the entire voting population.
While the wording of the question may be persuasive, the primary statistical error is the **non-random and restricted selection of respondents**. Response bias relates to how participants answer questions, whereas this issue arises before responses are even collected. Faulty operationalization and confusion of causality are not applicable here.
Data-driven decision making stresses ethical sampling practices to avoid misleading conclusions. Therefore, the correct answer is **D**, selection bias.
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NEW QUESTION # 38
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