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| Section | Objectives |
|---|---|
| Decision Making Models | - Decision trees and expected value analysis - Risk and uncertainty in decision-making |
| Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
| Regression and Correlation Analysis | - Linear regression modeling - Interpreting correlation and causation |
| Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
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NEW QUESTION # 27
A boutique specializing in gifts reviews its sales data over the last year. It observes a slow decline in revenue in the first quarter, a growth in revenue in the second quarter, a slight decline in revenue in the third quarter, and a rapid increase in revenue in the fourth quarter.
Which data pattern type can the sales data be assessed against?
Answer: D
Explanation:
Seasonalityrefers to predictable patterns in data that repeat at regular intervals, such as quarters or months, due to seasonal factors. In data-driven decision making, identifying seasonal patterns helps organizations forecast demand and plan operations.
The boutique's revenue shows distinct quarterly patterns: declines and increases that align with different times of the year. The sharp increase in the fourth quarter is especially indicative of seasonal effects, such as holiday shopping.
Random variation and irregularity describe unpredictable fluctuations, while cyclicality refers to long-term economic cycles rather than recurring annual patterns. Therefore, the correct answer isC, seasonality.
NEW QUESTION # 28
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 # 29
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: A
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 # 30
A plant manager wants to compare the production output for three assembly lines. Why is ANOVA the correct analysis technique to use for this scenario?
Answer: D
Explanation:
ANOVA, or analysis of variance, is the appropriate statistical technique when comparing the means of three or more groups. In this case, the plant manager wants to compare production output for three assembly lines, so ANOVA is the correct method because it can test whether the differences among the group means are statistically significant. It does not directly identify the reason for those differences, nor does it by itself determine the exact production rate mechanism. It also does not simply declare which assembly line has the most output without considering statistical variation. The strength of ANOVA is that it evaluates whether observed differences are likely due to actual process differences rather than random variation. If the ANOVA result is significant, further post hoc analysis may be used to determine which specific lines differ from one another. Therefore, the correct answer is that ANOVA can determine whether there is a significant difference in output among the assembly lines.
NEW QUESTION # 31
How should a marketing consulting firm perform a cluster analysis for a new granola bar?
Answer: A
NEW QUESTION # 32
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