A data scientist is performing EDA on a new dataset containing response times for millions of API calls, measured in milliseconds. The primary goal is to understand the underlying probability distribution of these response times to identify if there are distinct clusters of performance, such as bimodal or multimodal patterns, which might indicate different server behaviors. The analysis must be robust against arbitrary choices of binning that could misrepresent the true shape of the distribution. Which type of plot is most suitable for this specific task?
A density plot is the most appropriate choice. It visualizes the distribution of a continuous variable by creating a smooth curve using a Kernel Density Estimate (KDE). This method is ideal for observing the shape of a distribution, including identifying unimodal, bimodal, or multimodal patterns. Crucially, it is not subject to the binning problem that affects histograms, where the choice of bin width can significantly alter the visual representation of the distribution. Since the scenario's goal is to find the underlying shape and potential multiple peaks robustly, a density plot is the superior option.
Incorrect Answers:
Q-Q plot: A Quantile-Quantile (Q-Q) plot is a diagnostic tool used to compare the quantiles of a sample distribution against the quantiles of a theoretical distribution (e.g., a normal distribution). Its primary purpose is to assess if the data follows a specific, known distribution, not to explore the unknown shape or modality of the data itself.
Histogram: While a histogram can display the distribution of a single variable, its appearance is highly sensitive to the number and width of the bins selected. An inappropriate bin size can either hide important features like multiple peaks or create misleading visual artifacts. The scenario explicitly requires a method that is robust against this issue, making the histogram a less suitable choice than a density plot.
Scatter plot matrix: A scatter plot matrix is a multivariate analysis tool designed to show the pairwise relationships between two or more variables. The scenario describes a univariate analysis task-exploring the distribution of a single variable (API response time)-making a scatter plot matrix inappropriate for this specific goal.
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What is a Kernel Density Estimate (KDE) in a density plot?
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How does a density plot help identify multimodal distributions?