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| Section | Objectives |
|---|---|
| Topic 1: Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
| Topic 2: Regression and Correlation Analysis | - Linear regression modeling - Interpreting correlation and causation |
| Topic 3: Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
| Topic 4: Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Topic 5: Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
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NEW QUESTION # 41
A county government is creating a budget for the next fiscal year. They wish to use analytics to guide their decisions about costs.
Which analytic method can the county apply to this issue?
Answer: B
Explanation:
To guide budgeting decisions, data-driven decision making emphasizesbenchmarking against comparable organizations. Using theaverage cost per project spent by other similar countiesallows the county to assess whether its planned expenditures are reasonable and competitive.
Benchmarking provides external context that internal historical metrics cannot. While median costs or project counts describe internal performance, they do not indicate whether spending levels are appropriate relative to peers. Comparing average costs across similar counties helps identify inefficiencies, cost-saving opportunities, and realistic budget targets.
Therefore, optionAis the most effective analytic method for cost-based decision-making in this scenario.
NEW QUESTION # 42
Which two tools make it easier to detect an out-of-range error?
Choose 2 answers.
Answer: C,D
Explanation:
Out-of-range errors occur when a data value falls outside the allowable or expected limits for a variable.
Examples include a negative age, a score above the maximum possible value, or a date in an impossible format. The tools most useful for identifying such errors are relational databases and spreadsheets. Relational databases often include validation rules, field constraints, data types, and query capabilities that can detect impossible or invalid entries. For example, a database can restrict a field to numeric values within a set range or flag records that violate defined rules. Spreadsheets can also support error detection through conditional formatting, formulas, filters, data validation, and sorting features that make unusual values easier to spot.
Experimental studies and observational studies are research designs, not data-validation tools. They describe how data are collected, not how errors are detected in stored records. Because the question asks specifically for tools that make out-of-range errors easier to detect, the correct choices are the data-handling tools that support validation and review: relational databases and spreadsheets.
NEW QUESTION # 43
What is a statistical process control procedure for a drill manufacturer?
Answer: C
Explanation:
Statistical process control (SPC)focuses on monitoring production processes to ensure they remain within acceptable limits. In data-driven decision making, SPC uses control charts and statistical measures to detect variation and identify whether a process is operating as intended.
For a drill manufacturer, determining whether theweight of selected drills is within a tolerable rangeis a classic SPC activity. Consistent weight indicates stable materials and manufacturing processes, while deviations may signal defects or process drift.
Market segmentation and demand forecasting are strategic analytics tasks, not process control activities.
Collaborative planning forecasting and replenishment relates to supply chain coordination rather than manufacturing quality control.
Therefore, the correct answer isB, as SPC is concerned with maintaining process consistency and product quality.
NEW QUESTION # 44
Two project teams are assigned to upgrade an on-premise data warehouse to a cloud-based data lake in 13 months. The infrastructure team has five team members, while the enterprise analytics team has three team members. The enterprise analytics team cannot move into production until the infrastructure team has completed the migration.
What should be used to find the probability that the project will be completed on time?
Answer: A
Explanation:
This scenario requires the use of **conditional probability**, which applies when the likelihood of one event depends on the occurrence of another event. In data-driven decision making, conditional probability is used to model dependent events within processes, workflows, and project timelines.
In this case, the enterprise analytics team's ability to move into production is **dependent on** the infrastructure team completing the migration. Because one event cannot occur unless another event has already occurred, the probability of completing the project on time must account for this dependency.
The multiplication principle applies to independent events, Bayes' theorem updates probabilities based on new information, and combinations are used for counting outcomes, not dependency analysis. Conditional probability explicitly captures the relationship between dependent tasks.
Project risk analysis and scheduling often rely on conditional probability to assess completion likelihood when tasks are sequentially linked. Therefore, the correct answer is **C**, conditional probability.
NEW QUESTION # 45
A store owner wants to know the average sales for each day of the week. Which is this store owner looking for?
Answer: A
Explanation:
The term "average" in this context refers to the mean, which is the most common measure of central tendency for numerical data. To calculate the mean daily sales, the store owner would add the sales values for the days being studied and divide by the number of days. This provides a single summary value representing the typical sales amount across the week. Variance measures how much the daily sales differ from one another, not the average itself. Distribution describes the overall pattern of the data, such as whether sales are clustered, spread out, or skewed. Median identifies the middle value when observations are ordered from smallest to largest, which can be useful when data are highly skewed, but it is not the standard interpretation of "average" unless specifically stated. Because the question asks directly for the average sales for each day of the week, the most accurate statistical measure is the mean. Thus, the correct answer is mean, which summarizes daily sales using arithmetic average.
NEW QUESTION # 46
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