Data Analysis flashcards, generated for you.

A collection of data points or records, typically organized in rows (observations) and columns (variables), used as the foundation for analysis.

A characteristic or attribute that can take different values across observations. Can be categorical (qualitative) or numerical (quantitative).

Descriptive statistics summarize data using measures like mean and standard deviation. Inferential statistics use sample data to make conclusions about populations.

Retrieves specific columns and rows from a database table, forming the foundation of data extraction and analysis queries.

Filters rows based on specified conditions, allowing you to extract only data that meets particular criteria from a table.

A JOIN combines rows from two or more tables based on a shared column (key). Used to merge related data from multiple sources for unified analysis.

Correlation measures the strength and direction of a linear relationship between two variables. A correlation coefficient (e.g., Pearson's r) ranges from -1 to 1, where -1 is perfect negative, 0 is no relationship, and 1 is perfect positive.

Aggregation combines multiple rows into summary values (SUM, AVG, COUNT). Grouping (GROUP BY) partitions data into subsets before aggregation to analyze patterns within categories.

A null value represents missing or undefined data in a dataset. It matters because it can skew calculations, introduce bias, and requires handling (removal, imputation, or separate analysis) to ensure valid results.

Multicollinearity occurs when independent variables are highly correlated with each other, inflating standard errors, destabilizing coefficient estimates, and reducing model interpretability and reliability.