CBSE Class 11 Economics Chapter 7 Correlation NCERT Solutions

Reviewed by NCERT Help Editorial Team · Updated on 29 Jul 2026

NCERT Solutions PDF Class 11 PDF

This chapter delves into the concept of Correlation in Economics for Class 11 students following the CBSE curriculum. The NCERT Solutions provided here cover the fundamental aspects of correlation, including its measurement and interpretation. Students will learn about the correlation coefficient, its properties such as its range (from -1 to +1), and the meaning of positive, negative, and zero correlation. The solutions also touch upon different methods of measuring correlation, like Karl Pearson's coefficient and Spearman's rank correlation, and the suitability of each method. Understanding correlation is crucial for analyzing the relationship between economic variables. These solutions offer clear explanations and step-by-step guidance, making them an excellent resource for exam preparation and reinforcing conceptual clarity.

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Quick info

Board	CBSE
Class	Class 11
Subject	Economics
Session	2026
Language	English
Type	NCERT Solutions
Chapter	Chapter 7

Chapter summary

Chapter 7 of the Class 11 Economics syllabus focuses on Correlation. These NCERT Solutions explain the concept of correlation, which measures the degree of association between two variables. The solutions cover the properties of the correlation coefficient, including its range from -1 to +1, and the interpretation of positive, negative, and zero correlation. It also briefly introduces methods like Karl Pearson's coefficient and scatter diagrams for measuring correlation. This chapter is essential for understanding how economic variables move together.

Learning outcomes

Understand the concept of correlation coefficient.
Identify the range of the correlation coefficient.
Interpret the meaning of positive, negative, and zero correlation.
Differentiate between various measures of correlation.
Explain why the correlation coefficient is preferred over covariance.

Topics covered

Paper topics

Correlation Coefficient
Unit of Correlation Coefficient
Range of Correlation Coefficient
Positive Correlation
Negative Correlation
Zero Correlation
Linear Relationship
Non-linear Relationship
Karl Pearson's Coefficient of Correlation
Spearman's Rank Correlation
Scatter Diagram
Covariance vs. Correlation

Important topics

Range and Interpretation of Correlation Coefficient
Meaning of Positive, Negative, and Zero Correlation
Unitless Nature of Correlation Coefficient
Comparison of Correlation Measures

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Questions and Solutions

Question 1

The unit of correlation coefficient between height in feet and weight in kgs is:

(i). Kg/feet

(ii). Percentage

(iii). non-existent

Solution: The correct option is (iii) non-existent. The correlation coefficient is a statistical measure that quantifies the strength and direction of a linear relationship between two variables. It is a pure number and does not carry any units of measurement, regardless of the units of the variables being correlated (like feet for height and kgs for weight).

Question 2

The range of simple correlation coefficient is:

(i). 0 to infinity

(ii). Minus one to plus one

(iii). Minus infinity to infinity

Solution: The correct option is (ii) Minus one to plus one. The value of the simple correlation coefficient, often denoted by 'r', always lies between -1 and +1, inclusive. A value of +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship between the variables. If a calculated value falls outside this range, it suggests an error in the calculation.

Question 3

If $r_{xy}$ is positive, the relation between X and Y is of the type:

(i). When Y increases X increases

(ii). When Y decreases X increases

(iii). When Y increases X does not change

Solution: The correct option is (i) When Y increases X increases. A positive correlation coefficient (

r_{xy} > 0

) signifies that the variables X and Y tend to move in the same direction. This means that as the value of variable Y increases, the value of variable X also tends to increase, and conversely, as Y decreases, X also tends to decrease.

Question 4

If $r_{xy} = 0$ , the variables X and Y are:

(i). Linearly related

(ii). Not linearly related

(iii). Independent

Solution: The correct option is (ii) Not linearly related. A correlation coefficient of zero (

r_{xy} = 0

) indicates the absence of a linear relationship between variables X and Y. However, it is important to note that this does not necessarily mean the variables are independent. There might still be a non-linear relationship (e.g., a curvilinear relationship) between them.

Question 5

Of the following three measures, which can measure any type of relationship?

(i). Karl Pearson's coefficient of correlation

(ii). Spearman's rank correlation

(iii). Scatter diagram

Solution: The correct option is (ii) Spearman's rank correlation. While Karl Pearson's coefficient is widely used for measuring linear relationships and scatter diagrams visually represent relationships, Spearman's rank correlation is more versatile as it can measure both linear and non-linear relationships by analyzing the ranks of the data.

Question 6

If precisely measured data are available, the simple correlation coefficient is:

(i). More accurate than rank correlation coefficient

(ii). Less accurate than rank correlation coefficient

(iii). As accurate as the rank correlation coefficient

Solution: The correct option is (i) More accurate than rank correlation coefficient. When data is precisely measured and quantitative, Karl Pearson's coefficient of correlation, which uses the actual values, is generally considered more accurate and informative than Spearman's rank correlation, which relies on the ranks of the data. Spearman's rank correlation is more suitable for ordinal data or when the exact values are not available or when a non-linear relationship is suspected.

Question 7

Why is r preferred to covariance as a measure of association?

Solution: The correlation coefficient (r) is preferred over covariance as a measure of association primarily because it is a unitless measure, making it easier to interpret and compare across different datasets. Covariance, while indicating the direction of the relationship, is dependent on the units of the variables involved, making its magnitude difficult to interpret independently. The correlation coefficient normalizes the covariance by dividing it by the product of the standard deviations of the two variables, resulting in a standardized value that ranges from -1 to +1, thus providing a clear measure of the strength and direction of the linear association.

Common mistakes

Assuming zero correlation implies independence.
Confusing the units of correlation coefficient.
Incorrectly interpreting the range of the correlation coefficient.

Revision tips

Memorize the range of the correlation coefficient (-1 to +1).
Understand the implications of positive, negative, and zero correlation values.
Review the explanation for why correlation coefficient has no unit.
Clarify the difference between Karl Pearson's and Spearman's methods.

Practice MCQs

Q1. What is the unit of the correlation coefficient between height in feet and weight in kgs?

Q2. What is the possible range for the simple correlation coefficient?

Q3. If the correlation coefficient (r_xy) is positive, what type of relationship exists between variables X and Y?

Q4. When the correlation coefficient (r_xy) is 0, what can be concluded about the variables X and Y?

Q5. Which of the following measures can assess any type of relationship between variables?

Frequently asked questions

What is correlation in Economics?

Correlation in Economics refers to the statistical measure that describes the extent to which two or more economic variables fluctuate together. It indicates the direction and strength of a linear relationship between variables.

What is the range of the correlation coefficient?

The simple correlation coefficient (r) ranges from -1 to +1. A value of +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.

Does the correlation coefficient have any units?

No, the correlation coefficient is a unitless measure. It is a pure number that quantifies the degree of association between two variables, irrespective of their original units of measurement.

What does a positive correlation coefficient imply?

A positive correlation coefficient (r > 0) implies that the two variables tend to move in the same direction. When one variable increases, the other variable also tends to increase, and vice versa.

When is Karl Pearson's coefficient preferred over Spearman's rank correlation?

Karl Pearson's coefficient is generally preferred when the data is precisely measured and the relationship between variables is assumed to be linear. Spearman's rank correlation is useful for ordinal data or when the relationship is not strictly linear.

How do these NCERT solutions help in exam preparation?

These solutions provide clear, step-by-step explanations for each question, reinforcing understanding of correlation concepts. They help students practice interpreting correlation values and differentiate between various measurement methods, crucial for exam success.

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