3. Organisation of Data
The chapter discusses the importance of organizing raw data through classification for effective statistical analysis. It explains methods of classification, including frequency distribution, univariate and bivariate distributions, and highlights the significance of understanding continuous and discrete variables within this context. Through various examples, the chapter illustrates practical applications of data classification and its role in drawing meaningful conclusions from vast data sets.
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Sections
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What we have learnt
- Classification brings order to raw data.
- Frequency distribution shows how different values of a variable are distributed in various classes.
- Exclusive and inclusive methods define how class limits are determined.
- Statistical calculations in classified data are based on class midpoint values instead of individual observations.
Key Concepts
- -- Classification
- The process of arranging or organizing data into groups or classes based on specific criteria to facilitate statistical analysis.
- -- Frequency Distribution
- A comprehensive way to classify raw data of a quantitative variable, showing how different values are distributed across various classes.
- -- Bivariate Frequency Distribution
- A distribution that provides the frequency of two variables, allowing for the analysis of their relationship.
- -- Continuous Variable
- A variable that can take any numerical value within a given range, including fractions.
- -- Discrete Variable
- A variable that can take only specific, separate values, often whole numbers.
- -- Class Midpoint
- The average of the upper and lower class limits, used to represent a class in frequency distributions.
Additional Learning Materials
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