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| Section | Objectives |
|---|---|
| Topic 1: Hypothesis Testing | - Null and alternative hypotheses - t-tests, chi-square tests, and significance testing |
| Topic 2: Regression and Correlation Analysis | - Linear regression modeling - Interpreting correlation and causation |
| Topic 3: Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
| Topic 4: Probability and Statistical Inference | - Probability concepts and distributions - Sampling methods and sampling error |
| Topic 5: Decision Making Models | - Decision trees and expected value analysis - Risk and uncertainty in decision-making |
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NEW QUESTION # 57
A researcher seeks to pass a bond issue and asks a sample of respondents who have a bachelor's degree if they are voting in favor of the bond because it would be beneficial to the county.
Which type of error does this represent?
Answer: C
Explanation:
This scenario represents **selection bias**, which occurs when a sample is not representative of the population being studied. In data-driven decision making, valid conclusions depend on collecting data from a sample that accurately reflects the broader population.
By surveying only respondents with a bachelor's degree, the researcher systematically excludes other segments of the population who may have different opinions about the bond issue. Educational attainment may influence voting behavior, making the sample biased toward a particular viewpoint. As a result, the findings cannot be generalized to the entire voting population.
While the wording of the question may be persuasive, the primary statistical error is the **non-random and restricted selection of respondents**. Response bias relates to how participants answer questions, whereas this issue arises before responses are even collected. Faulty operationalization and confusion of causality are not applicable here.
Data-driven decision making stresses ethical sampling practices to avoid misleading conclusions. Therefore, the correct answer is **D**, selection bias.
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NEW QUESTION # 58
Why would a human resources department use both mean and median when doing a salary evaluation of a department?
Answer: C
Explanation:
Using both mean and median helps identifyoutliers, such as very high executive salaries that skew the average. A large difference between the two indicates uneven distribution.
Thus, the correct answer isC.
NEW QUESTION # 59
A financial analyst theorizes that commute times increase as the percentage of land availability for homes in a city decreases. To test this theory, the analyst uses a regression analysis. Which analysis result is supportive of this analyst's theory?
Answer: B
Explanation:
A regression result is most supportive of a theory when it shows a strong relationship between the independent and dependent variables. In this case, the analyst believes that commute times rise as land availability for homes falls. Among the answer choices, an R-squared value of 0.90 provides the strongest support because it indicates that about 90 percent of the variation in commute times is explained by the regression model. This suggests a very strong model fit. By contrast, an R-squared value of 0.10 would indicate a weak explanatory relationship. The p-values of 0.50 and 1 do not support the theory because large p- values suggest that the regression coefficient is not statistically significant. In regression analysis, a low p- value typically supports the idea that the predictor variable has a meaningful relationship with the outcome variable. Since no low p-value is offered, the best supportive result among the choices is the high R-squared value. Therefore, the correct answer is 0.90 because it indicates the model strongly explains the observed pattern.
NEW QUESTION # 60
What are random errors caused by?
Answer: D
Explanation:
Random errors are caused by unpredictable fluctuations that occur naturally in measurement, observation, or recording processes. These errors are not consistently in one direction and do not systematically push results higher or lower. Instead, they introduce variability that can make repeated measurements differ slightly even when conditions seem similar. Examples include minor environmental changes, momentary variations in instrument sensitivity, normal human reaction differences, or small observational inconsistencies. Because random errors are unsystematic, they tend to average out over a large number of observations, although they still reduce precision. By contrast, an instrument that needs calibration is more closely associated with systematic error, because it may consistently overstate or understate measurements. Respondents favoring certain outcomes and biased data also reflect systematic forms of bias rather than random variation. In statistics and quality measurement, distinguishing between random error and systematic error is important because each requires a different response. Random error is mainly addressed through repetition, sample size, and statistical controls, whereas systematic error must be corrected at the source. Therefore, the correct cause of random errors is unpredictable fluctuations in readings.
NEW QUESTION # 61
A manager has been assigned to manage a digital marketing analytics team. The manager tasks the team with determining similarities among existing customers in the company's database, such as similarities in products purchased, location, and the average amount spent per order among existing customers.
Which type of activity will help the team accomplish this task?
Answer: A
Explanation:
Data miningis the appropriate activity for identifying patterns, similarities, and relationships within large datasets. In data-driven decision making, data mining techniques such as clustering and association analysis are commonly used to segment customers based on behavior and characteristics.
The task described involves uncovering hidden patterns across multiple variables, which aligns directly with data mining objectives. Linear programming focuses on optimization, regression predicts outcomes, and touchpoint analysis examines customer interactions rather than similarities.
Therefore, the correct answer isA, data mining.
NEW QUESTION # 62
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