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| Section | Objectives |
|---|---|
| Topic 1: Regression and Correlation Analysis | - Linear regression modeling - Interpreting correlation and causation |
| Topic 2: Decision Making Models | - Decision trees and expected value analysis - Risk and uncertainty in decision-making |
| Topic 3: Probability and Statistical Inference | - Probability concepts and distributions - Sampling methods and sampling error |
| Topic 4: Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
| Topic 5: Hypothesis Testing | - Null and alternative hypotheses - t-tests, chi-square tests, and significance testing |
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NEW QUESTION # 118
A store owner wants to know the average sales for each day of the week. Which is this store owner looking for?
Answer: D
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 # 119
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 # 120
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: B,D
Explanation:
Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is thatmore time is spent managing data than analyzing data. Analysts must devote substantial effort to cleaning, validating, and correcting errors before meaningful analysis can occur, delaying insights and increasing costs.
Another critical result is thatmissing data tend to skew the results of the analysis. Incomplete data can distort averages, trends, and statistical relationships, leading to biased conclusions and unreliable decisions.
This is especially problematic in predictive and inferential analytics, where assumptions about data completeness are essential.
Using spreadsheets or placing data in charts does not inherently result from flawed data, nor does it resolve data quality issues. While visualization can help identify errors, it is not a direct outcome of starting with flawed data.
Data-driven decision making emphasizes that poor-quality input leads to poor-quality output. Ensuring data accuracy and completeness before analysis is essential for producing valid insights. Therefore, the correct answers areB and D.
NEW QUESTION # 121
Which tool is often referred to as a fishbone chart or diagram?
Answer: C
Explanation:
Thecause-and-effect diagram, also known as thefishbone diagramorIshikawa diagram, is used to identify potential causes of a problem. In data-driven decision making, it helps teams systematically explore root causes rather than focusing on symptoms.
The diagram visually organizes possible causes into categories such as people, processes, materials, machines, and environment. Histograms show distributions, flowcharts depict process steps, and scatter diagrams show relationships between variables.
Therefore, the correct answer isC, cause-and-effect diagram.
NEW QUESTION # 122
What is the 80/20 rule associated with a Pareto chart?
Answer: C
Explanation:
The80/20 rule, or Pareto principle, states that80% of problems result from 20% of causes. In data-driven decision making, this concept helps organizations prioritize efforts where they will have the greatest impact.
Pareto charts visually demonstrate this principle by showing that a small number of factors contribute disproportionately to overall problems. By addressing these critical few causes, organizations can achieve significant improvements with focused effort.
Therefore, the correct answer isD.
NEW QUESTION # 123
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