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| Section | Objectives |
|---|---|
| Topic 1: Data Analysis Fundamentals | - Data Types and Measurement Scales - Descriptive Statistics - Data Quality and Cleaning - Data Visualization Techniques |
| Topic 2: Data-Driven Culture and Communication | - Presenting Data Insights - Ethical Considerations in Data Use - Stakeholder Communication - Building Data-Informed Organizations |
| Topic 3: Decision-Making Frameworks | - Cost-Benefit Analysis - Rational Decision Models - Risk Analysis and Assessment - Evidence-Based Decision Making |
| Topic 4: Business Intelligence and Analytics | - Key Performance Indicators (KPIs) - Predictive Analytics Basics - Dashboards and Reporting - Data Mining Concepts |
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NEW QUESTION # 20
A clothing company wants to predict sales figures based on the amount spent on advertising.
Which type of regression analysis should this company use?
Answer: B
Explanation:
When predicting a continuous outcome based on a single predictor, data-driven decision making recommends simple linear regression. In this case, sales figures are continuous, and advertising spend is a single explanatory variable.
Linear regression models the relationship between one independent variable and one dependent variable by estimating a straight-line relationship. Time series regression is used when data are indexed over time, logistic regression is used for binary outcomes, and multiple linear regression requires multiple predictors.
Because the company is using only advertising spend to predict sales,linear regressionis the most appropriate method. Therefore, the correct answer isB.
NEW QUESTION # 21
For which situation could a scatter diagram be used?
Answer: D
Explanation:
Ascatter diagramis used to visually examine therelationship between two quantitative variables. In data- driven decision making, scatter diagrams help analysts assess whether variables move together, whether the relationship is positive, negative, or nonexistent, and whether the relationship appears linear or nonlinear.
Each point on a scatter diagram represents a paired observation of two variables, such as advertising spend and sales revenue or hours studied and test scores. Patterns in the plotted points can suggest correlation, which may later be explored using regression analysis. Scatter diagrams are exploratory tools and do not, by themselves, establish causation.
A prioritization matrix ranks options, frequency differences are examined using bar or Pareto charts, and differences in means are evaluated using hypothesis tests such as t-tests or ANOVA. Therefore, the correct application of a scatter diagram is to demonstraterelationships between variables, making optionBcorrect.
NEW QUESTION # 22
The daily sales from a salon are normally distributed with a mean of $1,500 and a standard deviation of $250.
The salon owner notices that sales were $750 on a particular day.
Why should the owner be concerned about sales based on this scenario?
Answer: C
Explanation:
In a normal distribution, most observations fall withinthree standard deviations of the mean. This principle is central to data-driven decision making and statistical process control. Values outside this range are considered highly unusual and may indicate an underlying problem.
Here, the mean is $1,500 and the standard deviation is $250. Three standard deviations below the mean equals
$750 ($1,500 # 3 × $250). Sales of exactly $750 fall at the extreme lower boundary, indicating an unusually low sales day.
Such an outcome signals a potential anomaly that warrants investigation, such as operational issues, staffing shortages, or external disruptions. Values this far from the mean occur very infrequently in normal conditions.
Therefore, the owner should be concerned because sales of $750 areoutside the typical operating range, making optionCthe correct answer.
NEW QUESTION # 23
Why is quantitative analysis important to the decision-making process?
Answer: D
Explanation:
Quantitative analysis is important in decision-making because it focuses on the systematic examination of measurable data, allowing organizations to evaluate situations objectively rather than relying only on intuition or personal judgment. A key strength of quantitative analysis is that it can examine and describe large sets of data in ways that reveal patterns, trends, relationships, and performance outcomes. This makes it especially useful in business, operations, finance, healthcare, and policy environments where decisions must be supported by evidence. By converting information into numbers, decision-makers can compare alternatives, estimate likely outcomes, and justify their choices with observable facts. While dashboards and surveys may support the process, they are not the fundamental reason quantitative analysis matters. Its real value lies in its ability to transform raw data into meaningful insights that improve planning, forecasting, risk evaluation, and resource allocation. Therefore, the best answer is the choice that identifies its central purpose: examining and describing large sets of data in a clear, measurable way.
NEW QUESTION # 24
Which distribution would have a mean and median that are approximately equal after an analysis of each employee's number of customer service calls over the last month?
Answer: C
Explanation:
In anormal distribution, data is symmetrically distributed around the center, causing the mean and median to be approximately equal. This is a fundamental property emphasized in data-driven decision making.
Skewed distributions, such as Pareto, cause the mean and median to differ significantly. Therefore, the correct answer isA.
NEW QUESTION # 25
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