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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Role of data in business decisions - Types of analytics: descriptive, predictive, prescriptive |
| Topic 2: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation |
| Topic 3: Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis |
| Topic 4: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value |
| Topic 5: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods |
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NEW QUESTION # 32
A retail manager collected the following sales-receipt totals from the store's cashiers:
$25, $22, $48, $42, $32, $28, $24, $54, $34, $41, $48
What is the median of this sales-receipt data?
Answer: D
Explanation:
Themedianis the middle value of a dataset when the data is arranged in ascending order. It is a key descriptive statistic used in data-driven decision making because it is resistant to extreme values.
First, sort the data in ascending order:
22, 24, 25, 28, 32,34, 41, 42, 48, 48, 54
There are 11 values in total, so the median is the 6th value. The 6th value is$34, making it the median.
The median provides insight into the typical transaction size without being influenced by unusually large receipts. Therefore, the correct answer isB.
NEW QUESTION # 33
Which two tools make it easier to detect an out-of-range error?
Choose 2 answers.
Answer: A,B
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 # 34
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: A,C
Explanation:
Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is that more 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 that missing 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 are B and D.
NEW QUESTION # 35
The U.S. Postal Service wants to know if local first-class mail is being delivered within two days of postmark.
Which key performance indicator (KPI) should the Postal Service use?
Answer: A
Explanation:
On-time performanceis the most appropriate KPI for measuring whether mail is delivered within a specified timeframe. In data-driven decision making, KPIs must align directly with operational objectives.
The Postal Service's goal is to assess delivery timeliness. On-time performance measures the percentage of mail delivered within the expected service standard, making it a direct and objective indicator.
Customer satisfaction and employee morale provide valuable insights but do not directly measure delivery speed. Incentive performance rate is unrelated to delivery outcomes.
Therefore, the correct answer isC, on-time performance.
NEW QUESTION # 36
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: B
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 # 37
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