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CPT Section D, Quantitative Aptitude, Chapter 12 
CA. Dharmendra Gupta 
Page 2


CPT Section D, Quantitative Aptitude, Chapter 12 
CA. Dharmendra Gupta 
Correlation is the 
relationship that exists 
between two or more 
variables. 
If two variables are 
related to each other in 
such a way that change 
increases a 
corresponding change in 
other, then variables are 
said to be correlated.  
Page 3


CPT Section D, Quantitative Aptitude, Chapter 12 
CA. Dharmendra Gupta 
Correlation is the 
relationship that exists 
between two or more 
variables. 
If two variables are 
related to each other in 
such a way that change 
increases a 
corresponding change in 
other, then variables are 
said to be correlated.  
Relationship between the heights and weights.  
Relationship between the quantum of rainfall and the yield 
of wheat. 
Relationship between the Price and demand of 
commodity. 
Relationship between the dose of insulin and blood sugar. 
Page 4


CPT Section D, Quantitative Aptitude, Chapter 12 
CA. Dharmendra Gupta 
Correlation is the 
relationship that exists 
between two or more 
variables. 
If two variables are 
related to each other in 
such a way that change 
increases a 
corresponding change in 
other, then variables are 
said to be correlated.  
Relationship between the heights and weights.  
Relationship between the quantum of rainfall and the yield 
of wheat. 
Relationship between the Price and demand of 
commodity. 
Relationship between the dose of insulin and blood sugar. 
Economic theory and business 
studies relationship between 
variables like price and quantity 
demand. 
Correlation analysis helps in 
deriving precisely the degree and 
the direction of such 
relationships. 
Page 5


CPT Section D, Quantitative Aptitude, Chapter 12 
CA. Dharmendra Gupta 
Correlation is the 
relationship that exists 
between two or more 
variables. 
If two variables are 
related to each other in 
such a way that change 
increases a 
corresponding change in 
other, then variables are 
said to be correlated.  
Relationship between the heights and weights.  
Relationship between the quantum of rainfall and the yield 
of wheat. 
Relationship between the Price and demand of 
commodity. 
Relationship between the dose of insulin and blood sugar. 
Economic theory and business 
studies relationship between 
variables like price and quantity 
demand. 
Correlation analysis helps in 
deriving precisely the degree and 
the direction of such 
relationships. 
The effect of 
correlation is to 
reduce the range of 
uncertainty of our 
prediction . 
The prediction based 
on correlation 
analysis will more 
reliable and near to 
reality. 
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FAQs on PPT - Correlation - Quantitative Aptitude for CA Foundation

1. What is correlation?
Ans. Correlation is a statistical measure that describes the relationship between two variables. It indicates the extent to which the variables are related and the direction of their relationship, whether it is positive, negative, or no relationship at all.
2. How is correlation calculated?
Ans. Correlation is calculated using a correlation coefficient, commonly denoted as "r". The formula for calculating the correlation coefficient depends on the type of data being analyzed. For example, Pearson's correlation coefficient is used for continuous variables, while Spearman's correlation coefficient is used for ordinal variables.
3. What does a correlation coefficient of 0 mean?
Ans. A correlation coefficient of 0 means that there is no linear relationship between the two variables being analyzed. It suggests that there is no association between the variables, and any observed relationship is due to chance.
4. Can correlation imply causation?
Ans. No, correlation does not imply causation. Just because two variables are correlated does not mean that one variable causes the other to change. Correlation only shows an association between variables, but it does not prove causality.
5. How can correlation be interpreted?
Ans. Correlation can be interpreted based on the value of the correlation coefficient. If the coefficient is close to 1, it indicates a strong positive linear relationship. If it is close to -1, it suggests a strong negative linear relationship. A coefficient close to 0 suggests no linear relationship. Additionally, the scatter plot of the data can provide visual interpretation of the correlation.
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