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| Section | Objectives |
|---|---|
| Topic 1: Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
| Topic 2: Probability and Statistical Inference | - Probability concepts and distributions - Sampling methods and sampling error |
| Topic 3: Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Topic 4: Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
| Topic 5: Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
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NEW QUESTION # 75
A county government wants to start a cost-effective recycling program. How can the county apply data analytic approaches to attain this goal?
Answer: A
Explanation:
To create a cost-effective recycling program, the county should benchmark similar strategies used by other counties. Benchmarking is a practical data-driven approach that allows decision-makers to compare program designs, costs, participation rates, collection methods, and outcomes across jurisdictions facing similar conditions. By examining how other counties structure successful recycling initiatives, the county can identify efficient practices, realistic cost expectations, and implementation strategies that have already been tested in comparable environments. This approach is especially useful when developing a new program because it reduces trial-and-error and supports evidence-based planning. The other options are less directly related to designing and evaluating a recycling program. Analyzing county contracts or aligning funds with staffing levels may be useful in general budgeting, but they do not specifically address recycling strategy effectiveness. Audit performance research is also not closely tied to recycling program design. Therefore, the most appropriate data analytic approach is benchmarking similar strategies of other counties.
NEW QUESTION # 76
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: C,D
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 # 77
A store owner wants to know the average sales for each day of the week. Which is this store owner looking for?
Answer: A
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 # 78
Why are experiments conducted using random sample populations?
Answer: D
Explanation:
Experiments and studies commonly use random samples because observing an entire population is often too difficult, costly, time-consuming, or impractical. In most real-world settings, researchers cannot collect data from every individual, item, or event of interest. A properly selected random sample allows them to estimate population characteristics with a manageable amount of effort while still preserving the ability to make statistical inferences. Random sampling improves representativeness and reduces selection bias, but it does not guarantee total elimination of bias. That is why the option claiming bias elimination is incorrect.
Likewise, the issue is not mainly about outliers or software cost. The main reason for using random samples is feasibility combined with inferential validity. With sound sampling methods, researchers can use probability theory to generalize findings from the sample to the broader population and estimate the degree of uncertainty in those conclusions. Therefore, the strongest and most accurate answer is that studying the entire population is difficult and impractical, making random sampling the preferred and efficient alternative.
NEW QUESTION # 79
Which performance metric simultaneously accounts for financial, customer, internal process, and learning metrics?
Answer: D
Explanation:
The balanced scorecard is the performance framework that simultaneously accounts for financial, customer, internal process, and learning and growth metrics. It was developed to provide a more complete view of organizational performance than financial measures alone. By incorporating these four perspectives, organizations can connect day-to-day activities with long-term strategy and ensure that performance is evaluated in a balanced way. Financial measures show economic results, customer measures reflect market and service outcomes, internal process measures track operational efficiency and quality, and learning metrics focus on improvement, capability development, and organizational growth. A customer complaint report addresses only one narrow area. A balance sheet and an income statement are financial documents and do not capture the full multidimensional view described in the question. Because the question asks for the metric that integrates all four of these important performance areas, the correct answer is balanced scorecard.
NEW QUESTION # 80
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