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Chapter Analysis
Intermediate16 pages • EnglishQuick Summary
Chapter 3, 'Organisation of Data,' introduces the importance of classifying data to facilitate accurate analysis and interpretation. It covers different types of data classification including quantitative and qualitative, and various methods for preparing a frequency distribution table. The chapter further explains the techniques for forming classes and using tally marks, as well as distinguishing between univariate and bivariate frequency distributions.
Key Topics
- •Classification of data
- •Quantitative vs qualitative classification
- •Frequency distribution
- •Class intervals
- •Tally marking
- •Univariate and bivariate frequency distributions
- •Discrete and continuous variables
- •Loss of information in data classification
Learning Objectives
- ✓Understand the need for organizing data
- ✓Differentiate between types of data classification
- ✓Construct and interpret frequency distributions
- ✓Select appropriate class intervals for data
- ✓Apply tally marks in frequency tabulation
- ✓Explain the implications of data classification
Questions in Chapter
Which of the following alternatives is true? (i) The class midpoint is equal to: (a) The average of the upper class limit and the lower class limit. (b) The product of upper class limit and the lower class limit. (c) The ratio of the upper class limit and the lower class limit. (d) None of the above.
Answer: The class midpoint is equal to the average of the upper class limit and the lower class limit.
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Can there be any advantage in classifying things? Explain with an example from your daily life.
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What is a variable? Distinguish between a discrete and a continuous variable.
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Explain the ‘exclusive’ and ‘inclusive’ methods used in classification of data.
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Use the data in Table 3.2 that relate to monthly household expenditure (in Rs) on food of 50 households and (i) Obtain the range of monthly household expenditure on food. (ii) Divide the range into appropriate number of class intervals and obtain the frequency distribution of expenditure. (iii) Find the number of households whose monthly expenditure on food is (a) less than Rs 2000 (b) more than Rs 3000 (c) between Rs 1500 and Rs 2500.
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In a city 45 families were surveyed for the number of Cell phones they used. Prepare a frequency array based on their replies as recorded.
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What is ‘loss of information’ in classified data?
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Do you agree that classified data is better than raw data? Why?
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Distinguish between univariate and bivariate frequency distribution.
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Prepare a frequency distribution by inclusive method taking class interval of 7 from the given data.
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Additional Practice Questions
Explain the significance of frequency distribution in the analysis of data.
mediumAnswer: Frequency distribution helps to summarize large data sets by organizing the data into classes and showing the number of observations in each class. This provides a clear and concise overview of the data, enabling easier identification of trends and patterns.
Discuss the difference between qualitative and quantitative classification with examples.
mediumAnswer: Qualitative classification organizes data based on non-numeric categories such as color or type, e.g., categorizing books by genre. Quantitative classification arranges data based on numeric characteristics, such as classifying incomes into different ranges.
How can tally marks be useful when organizing data?
easyAnswer: Tally marks offer a simple and efficient method for keeping count of occurrences, especially when organizing data into frequency distributions. They allow quick visual assessment and summation, reducing potential counting errors.
Create a frequency distribution from this data: [23, 41, 22, 30, 22, 32, 41, 30].
mediumAnswer: The frequency distribution is: 22 appears 2 times, 23 appears 1 time, 30 appears 2 times, 32 appears 1 time, 41 appears 2 times.
What challenges might arise from using improper class intervals in data classification?
hardAnswer: Improper class intervals can lead to misleading interpretations as they might over-simplify or over-complicate the data, leading to loss of information or clarity. They might also fail to represent data trends adequately.