Society of Actuaries (SOA) PA Practice Exam

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In regression analysis, what does granularity refer to?

The number of predictors available
The level of detail of measurement for a variable
The statistical significance of the model
The proportion of explained variance

The correct answer is: The level of detail of measurement for a variable

Granularity in regression analysis refers to the level of detail of measurement for a variable. It denotes how finely data is broken down and can have significant implications for the analysis. For instance, if you are measuring sales data, having daily sales figures represents a higher granularity compared to monthly aggregates. Higher granularity allows for more precise analysis and better insight into patterns and relationships in the data because it can capture variability that would be masked in aggregate data. Conversely, lower granularity may overlook important nuances and lead to less accurate or less insightful conclusions. Thus, it is crucial to consider the granularity of the data when setting up a regression model to ensure it is appropriate for the questions being asked or the phenomena being studied.