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| Section | Objectives |
|---|---|
| Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
| Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
| Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
| Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
| Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
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NEW QUESTION # 111
What is true about outliers?
Choose 2 answers.
Answer: C,D
Explanation:
Outliers are observations that differ substantially from the rest of the data, but they are not automatically errors and should not be removed without investigation. One correct statement is that miskeyed outliers can be corrected before analysis. For example, if a value of 500 is entered instead of 50, this is a data-entry error, not a meaningful observation, and it should be fixed using source verification. Another correct statement is that outliers can help determine whether something does not belong in the study. They may reveal invalid records, unusual conditions, a different population, process breakdowns, or rare but important events. The incorrect choices are too absolute. Not all outliers are statistically significant, even under a normal distribution, because significance depends on context, sample size, and method. Likewise, not all observed outliers should be eliminated. Some outliers contain valuable information and can indicate real variation that deserves attention. In quality control, fraud detection, medical screening, and operational monitoring, outliers may be among the most important data points. Therefore, the correct answers are the ones that recognize both correction of miskeyed values and the analytical value of identifying unusual observations.
NEW QUESTION # 112
What does prevalence measure when studying disease frequency?
Answer: A
Explanation:
Prevalence measures all existing cases of a disease within a population at a given point in time or over a specified period. It includes both newly diagnosed cases and pre-existing cases that are still present. This makes prevalence a measure of the total disease burden in the population rather than a measure of the rate at which new cases arise. In public health and epidemiology, prevalence is especially useful for understanding how widespread a condition is and for planning healthcare services, allocating resources, and evaluating community health needs. It differs from incidence, which focuses only on new cases occurring during a specified time period. Because the question asks what prevalence measures, the correct answer is the one that captures all current cases, not just past or new ones. It also does not refer to potential future cases, since prevalence is based on observed disease status. Therefore, the best answer is all existing cases of a disease.
NEW QUESTION # 113
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: A
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 # 114
Which two results occur when the null hypothesis is accepted using an F-test?
Choose 2 answers.
Answer: A,C
Explanation:
When the null hypothesis is accepted in anF-test, it indicates that there is no statistically significant difference between group variances or means, depending on the test design. Acceptance occurs when thetest statistic is less than the critical value, meaning the observed variation is within expected limits.
Accepting the null hypothesis implies thatno meaningful differenceexists between the samples. If the test statistic exceeded the critical value, the null hypothesis would be rejected.
Thus, the correct results areA and D.
NEW QUESTION # 115
A car dealership sells both new and used cars. The number of new cars sold on a given day ranges from 5 to
30 while the number of used cars sold ranges from 5 to 40. The number of used cars sold is mutually exclusive to the number of new cars sold.
Which statistic would be used to compare the number of new and used car sales on any given day?
Answer: B
Explanation:
Thechi-square statisticis used to compare frequencies of categorical, mutually exclusive outcomes. In data- driven decision making, it is appropriate for analyzing differences between observed counts.
New and used car sales represent mutually exclusive categories, making chi-square the correct choice.
Therefore, the correct answer isB.
NEW QUESTION # 116
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