Answer-first summary for fast verification
Answer: Central limit theorem, because it allows the approximation of the distribution of sample means to a normal distribution, facilitating inference about the population from sample data.
**Correct Option: B. Central Limit Theorem** The Central Limit Theorem (CLT) is essential for understanding the probability distribution of a dataset, especially in scenarios involving large and skewed datasets like customer transaction data. It justifies the use of normal distribution-based methods for inference, even when the underlying data does not follow a normal distribution, by ensuring that the distribution of sample means approximates a normal distribution as the sample size increases. This is crucial for fraud detection as it allows for the application of statistical tests and confidence intervals to identify anomalous transactions. **Why other options are not correct**: - **A. Principal component analysis**: While useful for dimensionality reduction, it does not directly help in understanding the probability distribution of the data. - **C. Linear regression**: This is a predictive modeling technique, not a tool for understanding data distributions. - **D. K-means clustering**: This is an unsupervised learning technique for grouping data, not for understanding their probability distributions.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
In the context of preparing a machine learning model for a financial services company, you are tasked with selecting the most appropriate statistical concept to understand the probability distribution of customer transaction data. The dataset is large and skewed, with the goal of improving fraud detection. Which of the following concepts is crucial for this task? Choose the best option.
A
Principal component analysis, as it reduces the dimensionality of the dataset, making it easier to visualize and understand the distribution.
B
Central limit theorem, because it allows the approximation of the distribution of sample means to a normal distribution, facilitating inference about the population from sample data.
C
Linear regression, for modeling the relationship between transaction amounts and the likelihood of fraud.
D
K-means clustering, to group similar transactions together based on their features.