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| Section | Objectives |
|---|---|
| Topic 1: Decision Making Models | - Decision trees and expected value analysis - Risk and uncertainty in decision-making |
| Topic 2: Data Fundamentals and Business Analytics | - Data types and data collection methods - Descriptive statistics (mean, median, variance, standard deviation) |
| Topic 3: Regression and Correlation Analysis | - Linear regression modeling - Interpreting correlation and causation |
| Topic 4: Probability and Statistical Inference | - Probability concepts and distributions - Sampling methods and sampling error |
| Topic 5: Hypothesis Testing | - Null and alternative hypotheses - t-tests, chi-square tests, and significance testing |
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NEW QUESTION # 118
Which step in the plan-do-check-act cycle is described as analyzing the results of an experiment and deciding whether those results can be improved?
Answer: A
Explanation:
TheCheckphase of the Plan-Do-Check-Act (PDCA) cycle involves evaluating outcomes and analyzing results. In data-driven decision making, this step compares actual performance against expected results to determine whether objectives were met.
During the Check phase, organizations review data, assess variation, and identify opportunities for improvement. Planning defines objectives, Doing implements changes, and Acting standardizes or adjusts processes based on evaluation.
Therefore, the correct answer isA, Check.
NEW QUESTION # 119
A spa wants to determine which duration of its massage packages is the most popular. The spa has 30-minute,
50-minute, and 75-minute sessions. Which statistical measure should be used and is less affected by outliers and skewed data?
Answer: C
Explanation:
The spa wants to know which package duration is the most popular, meaning which session length occurs most frequently in the data. The statistical measure used to identify the most frequently occurring category or value is the mode. Because the massage durations are offered in discrete categories of 30, 50, and 75 minutes, the mode is the best measure for determining customer preference. It directly identifies the package selected most often. Standard deviation measures variability, not popularity. Mean and median are measures of central tendency, but they do not answer the question of which option is chosen most frequently. Although the wording mentions being less affected by outliers and skewed data, the strongest reason to choose the mode is that popularity is fundamentally a frequency question. The mode is especially useful for categorical or discrete-choice data where the goal is to identify the most common option rather than the center of a numerical distribution. Therefore, the correct answer is mode because it shows which massage duration customers prefer most often.
NEW QUESTION # 120
Which use of statistics would apply to employees?
Answer: B
Explanation:
Statistics can be applied to employees and organizations in many ways, but among the choices given, predicting future levels of financial risk is the best fit for a practical statistical use. Organizations often use statistical models to evaluate uncertainty related to staffing, benefits, payroll obligations, productivity changes, turnover, insurance exposure, and broader business performance. These analyses help managers make more informed decisions about budgeting, hiring, workforce planning, and operational resilience. The other options are less directly tied to employee-related statistical application. Influencing vendor prices and comparing wholesale pricing are more related to procurement and market analysis than to employees.
Determining financial interest rates generally falls under financial markets, lending, or macroeconomic policy rather than an employee-centered use of statistics. In a data-driven environment, statistical tools are frequently used to forecast risk and evaluate future scenarios so that organizations can protect resources and plan responsibly. Therefore, predicting future levels of financial risk is the most accurate answer because it reflects a recognized analytical application of statistics within organizational decision-making.
NEW QUESTION # 121
How is a cost-benefit analysis different in the public and private sectors?
Answer: A
Explanation:
Cost-benefit analysis differs between the public and private sectors primarily because the goals of the two sectors are different. In the public sector, decisions are generally evaluated in terms of the general welfare of the population, including social value, public health, safety, infrastructure, education, and broader community outcomes. In the private sector, cost-benefit analysis is usually more focused on profitability, financial return, efficiency, and shareholder value. While both sectors consider costs and benefits, the definition of "benefit" often changes depending on the mission of the organization. Public-sector benefits may include social improvements that do not generate direct profit, whereas private-sector benefits are often measured through revenue, cost savings, or market performance. The other options are too narrow or incorrect because they suggest one-sided attention to only cost or only benefits. Therefore, the best answer is that the public sector usually focuses on the general welfare of the population, whereas the private sector usually focuses on profits.
NEW QUESTION # 122
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 # 123
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