CBSE 11 Statistics for Economics | 3. Organisation of Data by Pavan | Learn Smarter
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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

  • 3

    Organisation Of Data

    This section introduces the organisation of data, focusing on the classification of raw data for statistical analysis.

    Learning Practice

  • 3.1

    Introduction

    This section introduces the organisation of data, focusing on the classification of raw data for statistical analysis.

    Learning Practice

  • 3.2

    Raw Data

    This section discusses the importance of organizing raw data for statistical analysis, highlighting the need for classification and frequency distribution.

    Learning Practice

  • 3.3

    Classification Of Data

    This section explains how data can be systematically organized for analysis, focusing on various methods of classification, including qualitative and quantitative approaches.

    Learning Practice

  • 3.4

    Variables: Continuous And Discrete

    This section explores the classification of variables into continuous and discrete types, detailing their characteristics and relevance in data analysis.

    Learning Practice

  • 3.5

    What Is A Frequency Distribution?

    A frequency distribution organizes unclassified data into structured classes to facilitate statistical analysis.

    Learning Practice

  • 3.5.1

    How To Prepare A Frequency Distribution

    This section teaches how to prepare a frequency distribution table, organizing raw data into classes for statistical analysis.

    Learning Practice

  • 3.5.2

    Should We Have Equal Or Unequal Sized Class Intervals?

    This section discusses the advantages and disadvantages of using equal versus unequal class intervals in frequency distribution.

    Learning Practice

  • 3.5.3

    How Many Classes Should We Have?

    This section discusses the importance of classifying data for statistical analysis and the different methods of classification.

    Learning Practice

  • 3.5.4

    What Should Be The Size Of Each Class?

    The size of each class is determined by the range of data and the number of classes, which are interdependent.

    Learning Practice

  • 3.5.5

    How Should We Determine The Class Limits?

    Class limits should be clearly defined to ensure data frequencies are concentrated appropriately within the class intervals.

    Learning Practice

  • 3.5.6

    Adjustment In Class Interval

    This section discusses how to adjust class intervals in frequency distributions to eliminate gaps and ensure continuity in the classification of continuous variables.

    Learning Practice

  • 3.5.7

    How Should We Get The Frequency For Each Class?

    Frequency refers to how often an observation appears in the raw data, while class frequency counts the number of values in a specific class.

    Learning Practice

  • 3.5.8

    Finding Class Frequency By Tally Marking

    This section discusses the process of classifying raw data into meaningful frequency distributions using tally marking.

    Learning Practice

  • 3.5.9

    Loss Of Information

    This section discusses how classifying raw data into frequency distributions leads to a loss of information.

    Learning Practice

  • 3.5.10

    Frequency Distribution With Unequal Classes

    The section discusses the construction and significance of frequency distributions with unequal classes, emphasizing their practical applications in statistics.

    Learning Practice

  • 3.5.11

    Frequency Array

    This section elaborates on the classification of data into frequency distributions, emphasizing the importance of organizing raw data to facilitate statistical analysis.

    Learning Practice

  • 3.6

    Bivariate Frequency Distribution

    Bivariate Frequency Distribution summarizes data involving two variables, allowing for a better understanding of their relationship through organized data representation.

    Learning Practice

  • 3.7

    Conclusion

    The conclusion emphasizes the importance of data classification for effective statistical analysis.

    Learning Practice

References

NCERT Study Material

Class Notes

Memorization

What we have learnt

  • Classification brings order...
  • Frequency distribution show...
  • Exclusive and inclusive met...

Final Test

Revision Tests