Organisation of Data  Class 11 Economics  Chapter 3  Notes, NCERT Solutions & Extra Questions
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Notes  Organisation of Data  Class 11 Statistics For Economics  Economics
Comprehensive Organisation of Data Notes for Class 11
Understanding and organising data is fundamental to the study of statistics. This guide provides Class 11 students with indepth notes on the organisation of data, geared towards making the concepts clear and applicable for further statistical analysis. Let's delve into the various aspects of data organisation.
Introduction
Understanding the Organisation of Data
The organisation of data entails systematically arranging collected data to facilitate further analysis. This process is essential as it helps in making data comprehensible and manageable.
Importance of Data Organisation in Statistics
Organising data is crucial because raw data can be highly disorganised, voluminous, and cumbersome to handle. Proper organisation helps in:
 Easier data analysis
 Quick retrieval of information
 Accurate and efficient statistical conclusions
Raw Data
What is Raw Data?
Raw data refers to data collected in its original form, unorganised and unprocessed. It is often large, scattered, and not immediately usable for statistical analysis.
Characteristics of Raw Data
 Unordered: Randomly listed without any particular sequence.
 Voluminous: Large amounts of data points.
 Difficult to interpret: Cannot yield meaningful information directly.
Challenges in Handling Raw Data
Handling raw data can be painstaking due to its disorganised nature. This necessitates the refinement of data through classification.
Classification of Data
Classification is the systematic grouping of raw data into different categories based on certain criteria. Here's a flowchart that illustrates how raw data is classified:
graph TD;
A[Raw Data] > B[Chronological Classification]
A > C[Spatial Classification]
A > D[Qualitative Classification]
A > E[Quantitative Classification]
Classification brings order to data, enabling more straightforward analysis and comprehension.
Types of Data Classification

Chronological Classification
 Definition: Grouping data based on time periods such as years, months, weeks, etc.

Example:
Year Population (Crores) 1951 35.7 1961 43.8 1971 54.6

Spatial Classification
 Definition: Grouping data based on geographical areas like continents, countries, states, cities, etc.

Example:
Country Yield of Wheat (kg/hectare) Canada 3594 China 5055 India 3154

Qualitative Classification
 Definition: Grouping data based on qualitative attributes that cannot be measured, such as gender, religion, or marital status.

Example:
Gender Marital Status Male Married, Unmarried Female Married, Unmarried

Quantitative Classification
 Definition: Grouping data based on quantitative measures, such as height, weight, income, etc.

Example:
Marks Frequency 010 1 1020 8 2030 6
Variables in Data
A variable is an attribute that varies from one individual to another. They can be broadly classified into:

Continuous Variables
 Definition: Variables that can take any value within a given range, including fractions.
 Examples: Height, weight, time.

Discrete Variables
 Definition: Variables that can take only specific values and cannot take values in between those specific points.
 Examples: Number of students in a class, number of books.
Frequency Distribution
What is a Frequency Distribution?
A frequency distribution displays the number of observations of a variable within different intervals. It effectively summarises large data sets and highlights trends.
Steps to Create a Frequency Distribution Table
 Decide the number of classes
 Determine the class interval
 Set the class limits
 Count the frequency for each class using tally marks
Importance of Class Intervals and Class Limits
Class intervals and limits define the range within which data points fall. The intervals should be nonoverlapping and comprehensive to cover all data points.
Using Tally Marks in Frequency Distribution
Tally marks make it simpler to count and record observations, especially when dealing with large data sets.
Frequency Curve and Its Significance
A frequency curve graphically represents the frequency distribution, depicting how data points are distributed and assisting in visual data analysis.
Special Types of Distributions
Bivariate Frequency Distribution
A bivariate frequency distribution involves two variables, helping to understand the relationship between them.
Example of Bivariate Distribution
Consider a sample of companies where data on sales and advertisement expenditure are recorded. A bivariate distribution table categorises the data based on these two attributes.
Practical Applications
RealLife Examples of Data Classification
 Educational Settings: Classifying students' marks, attendance records.
 Business and Economics: Grouping sales data, employee performance metrics.
Summary
Key Takeaways
 Data organisation is vital for statistical analysis
 Different types of classification serve unique purposes
 Understanding the nature of variables aids in appropriate classification
 Frequency distributions simplify data complexity
Final Thoughts on Data Organisation for Students
Mastering data organisation is a fundamental step towards proficient statistical analysis. It offers a structured approach to handling large volumes of data, making insights more accessible and precise.
Conclusion
Proper classification and organisation of data are pivotal in transforming raw data into meaningful information. As you advance in your studies, these concepts will serve as the foundation for more complex statistical analyses. Continue exploring and practising these techniques to enhance your analytical skills.
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Extra Questions  Organisation of Data  Statistics For Economics  Economics  Class 11
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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.
(a) The average of the upper class limit and the lower class limit.
The frequency distribution of two variables is known as.............
(a) Univariate Distribution
(b) Bivariate Distribution
(c) Multivariate Distribution
(d) None of the above
(b) Bivariate Distribution
Statistical calculations in classified data are based on..............
(a) the actual values of observations
(b) the upper class limits
(c) the lower class limits
(d) the class midpoints
(d) the class midpoints
Range is the....................
(a) difference between the largest and the smallest observations
(b) difference between the smallest and the largest observations
(c) average of the largest and the smallest observations
(d) ratio of the largest to the smallest observation
(a) difference between the largest and the smallest observations
Can there be any advantage in classifying things? Explain with an example from your daily life.
Yes, Classifying objects or information in daily life simplifies management, organization, and retrieval, making activities more efficient and less timeconsuming. For example, consider organizing books on shelves. By classifying them into categories such as fiction, nonfiction, and academic texts, one can easily locate a specific book without searching through an unordered pile. This not only saves time but also reduces clutter and enhances the visual appeal of the space. In schools, students classify their subjects into different notebooks, which optimizes study time and improves material accessibility, thus directly impacting their academic performance positively.
What is a variable? Distinguish between a discrete and a continuous variable.
A variable represents a characteristic, number, or quantity that can change or vary in an economic context.
Discrete Variable:
Takes specific, distinct values.
Often countable (e.g., number of customers, number of products sold).
Continuous Variable:
Can take any value within a range.
Measurable, not countable (e.g., income, price, weight).
In summary, discrete variables have distinct values, while continuous variables can take any value within a given range.
Explain the ‘exclusive’ and ‘inclusive’ methods used in classification of data.
Exclusive Method: In this method of classification, the class intervals are defined so that the upper limit of one class is the same as the lower limit of the next class, but it's not included within the class itself. For example, 1020, 2030, etc.
Inclusive Method: Here, both the lower and upper limits of the class intervals are included in the class. For example, 1019, 2029, etc.
These methods help in organizing data effectively depending on whether limits are to be strictly separated or shared within class intervals.
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
(i) Obtain the Range of Monthly Household Expenditure on Food
Range is the difference between the maximum and minimum values in the dataset.
From the table:
Minimum expenditure = Rs 1007
Maximum expenditure = Rs 5090
Range = Maximum  Minimum = 5090  1007 = Rs 4083
(ii) Divide the Range into Appropriate Number of Class Intervals and Obtain the Frequency Distribution of Expenditure
To create a frequency distribution, we typically divide the range into equal class intervals. We'll choose a reasonable class width for easier interpretation. The class width can be determined based on the data's range (as calculated) and the data's practicality.
Given the range of 4083, let's choose a class interval of Rs 500. This provides us:
$$\text{Number of intervals} = \frac{\text{Range}}{\text{Class width}} = \frac{4083}{500} \approx 9$$
Class Intervals  Tally Marks  Frequency 

$10001500$ 
 20 
$15002000$ 
 13 
$20002500$ 
 06 
$25003000$ 
 05 
$30003500$  II  02 
$35004000$  I  01 
$40004500$  II  02 
$45005000$    00 
$50005500$  I  01 
Total  50 
Note: The frequencies are estimated from the provided dataset by counting the number of entries falling into each class interval.
(iii) Find the Number of Households
(a) Less than Rs 2000
This includes intervals from Rs 10001499 and Rs 15001999.
 Frequency: 20 + 13 = 33 households
(b) More than Rs 3000
This includes intervals from Rs 30003499 to Rs 50005499.
 Frequency: 2 + 1 + 2 + 1 = 6 households
(c) Between Rs 1500 and Rs 2500
This includes intervals from Rs 15001999 and Rs 20002499.
 Frequency: 13 + 6 = 19 households
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 below.
1  3  2  2  2  2  1  2  1  2  2  3  3  3  3 
3  3  2  3  2  2  6  1  6  2  1  5  1  5  3 
2  4  2  7  4  2  4  3  4  2  0  3  1  4  3 
To prepare a frequency array for the number of cell phones used by the 45 families surveyed, we need to count the occurrences of each number of cell phones reported. Here is the frequency array based on the given data:
Number of Cell Phones  Frequency 

0  1 
1  7 
2  15 
3  12 
4  5 
5  2 
6  2 
7  1 
Total  45 
This table represents the frequency distribution of the number of cell phones used by the surveyed families.
What is ‘loss of information’ in classified data?
'Loss of information' in classified data refers to the reduction in data quality and utility due to the process of transforming detailed and precise data into more generalized categories. This often happens to preserve confidentiality or for easier analysis, but can lead to important nuances and specific details being omitted, resulting in less precise insights and potential misinterpretation of the data.
Do you agree that classified data is better than raw data? Why?
Yes, classified data is often better than raw data because classified data is organized into categories, making it easier to analyze and interpret. Here are a few reasons:
Clarity: Classified data helps in summarizing information, making complex data sets more understandable.
Efficiency: It reduces the volume of data to be handled, saving time and resources.
Comparison: Enables easier comparison and identification of trends or patterns.
DecisionMaking: Facilitates betterinformed decisions by providing structured insights.
However, the specific context and purpose of the data analysis should guide whether classified or raw data is more appropriate.
Distinguish between univariate and bivariate frequency distribution.
A univariate frequency distribution involves only one variable, presenting the frequency of each value or range of values for that single variable.
Example: A table showing the number of students scoring different grades in a test.
A bivariate frequency distribution involves two variables and shows the frequency of combinations of different values or ranges for these two variables.
Example: A table showing the number of students who scored different combinations of math and science grades.
Prepare a frequency distribution by inclusive method taking class interval of 7 from the following data.
28  17  15  22  29  21  23  27  18  12  7  2  9  4 
1  8  3  10  5  20  16  12  8  4  33  27  21  15 
3  36  27  18  9  2  4  6  32  31  29  18  14  13 
15  11  9  7  1  5  37  32  28  26  24  20  19  25 
19  20  6  9 
Class  Tally Marks  Frequency 

07 
 15 
815 
 15 
1623 
 14 
2431 
 11 
3239 
 5 
Total  60 
Table
“The quick brown fox jumps over the lazy dog” Examine the above sentence carefully and note the numbers of letters in each word. Treating the number of letters as a variable, prepare a frequency array for this data.
Let's first identify the number of letters in each word of the sentence:
The: 3 letters
quick: 5 letters
brown: 5 letters
fox: 3 letters
jumps: 5 letters
over: 4 letters
the: 3 letters
lazy: 4 letters
dog: 3 letters
Number of Letters  Frequency 

3  4 
4  2 
5  3 
Frequency Array:
3 letters: 4
4 letters: 2
5 letters: 3
Interpretation: The table shows that most words (4 out of 9) contain 3 letters. Meanwhile, words with 5 letters are the next most common, with 3 occurrences, followed by words with 4 letters totalling 2 occurrences.
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