Correlation Analysis Notes For MBA 1st Year Semester Very Short Question Answer

Correlation Analysis Notes For MBA 1st Year Semester Very Short Question Answer Study Material Notes Sample Model Practise Notes Rank Method And Kari Pearson’s Coefficient Of Correlation And Properties Of Correlation Regression And Properties Of Correlation. Regression Analysis Fitting Of A Regression Line And Interpretation Of Results, Properties Of Regression Coefficients And Relationship Between Regression And Correlation.

Correlation Analysis Notes For MBA 1st Year Semester Very Short Question Answer
Correlation Analysis Notes For MBA 1st Year Semester Very Short Question Answer

Section A

VERY SHORT ANSWER QUESTIONS

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MBA Topic Chapter Semester Wise Sample Model Practice Question Answer Papers

Q.1. What is meant by correlation?

Ans. The term correlation can be defined as the degree of interdependence between two variable Two variables are said to be correlated when the change in the value of one variable leads to the change in the values of the other. In other words, two variables of the same group are said to be correlated when an increase in the value of one variable leads to an increase in the value of the other or an increase in the value of one variable leads to the decrease in the value of the other or the decrease in the value of one variable, leads to the decrease in the value of the other or decrease in the value of one variable leads to an increase in the value of the other variables.

Q.2. Define the term positive correlation.

Ans. When an increase in the value of one variable leads to an increase in the value of the other variable and when the decrease in the value of one variable leads to the decrease in the value of the other variable, the correlation between the two variables is said to be positive correlation.

Q.3. What is the meaning of perfect correlation?

Ans. When the change in the values of two related variables is in the same direction and same proportion, the correlation is called perfect positive correlation. The coefficient of correlation in this case is +1. On the other hand, when the value of the two related variables change in same proportion but in opposite direction, the correlation is called perfect negative correlation. The coefficient of correlation in this case is -1.

Q.4. What is the Karl Pearson’s coefficient of correlation?

Ans. This method of measuring correlation was given by Karl Pearson in 1896. This method is also known as ‘Pearson coefficient of correlation’ or ‘Product moment method of correlation’. It is one of the most widely used mathematical methods of computing correlation. It clearly explains the degree and direction of relationship between two variables. The Karl Pearson’s coefficient is denoted as’y and it is computed on the basis of mean and standard deviation.

Q.5. Write the three assumptions of Karl Pearson’s coefficient of correlation. 

Ans. The three assumptions of Karl Pearson’s coefficient of correlation are:

1. Normality. 

2. Cause and effect relationship. 

3. Linear relationship.

Q.6.Walte the two merits and demerits of Spearman’s rank difference method.

Ans. Merits: These are as follows:

  1. This method is easy to calculate and simple to understand as compared to Karl Pearson’s method.
  2. This is the only method which can be used in calculating coefficient of correlation.

Q.7. Give any two merits and demerits of coefficient of concurrent deviation method. 

Ans. Merits: The two merits are:

1. It is the simplest of all the methods and it is also easy to understand. 

2. This method is specifically useful when the number of items are very large. 

Demerits: The two demerits are: 

1. In this method, only the direction of change is studied and the magnitude of change is completely ignored. 

2. It is a rough medicator of correlation. 

Q.8. What are regression lines?

Ans. The lines which give the best estimate of the value of one variable for any given value of the other variable are called the ‘Lines of regression’ or ‘Regression lines’. In other words, regression lines are used to predict the value of dependent variable when the value of independent variable is known.

Q.9. Write the three functions of regression lines. 

Ans. The three functions of regression lines are:

1. They indicate the degree and direction of correlation. 

2. They are useful in predicting the value of the dependent variable when the value of independent variable is known. 

3. They are helpful in calculation of mean value as the perpendiculars drawn at the point where the two regression lines cut each other, are the mean value of the two variables. 

Q.10. Define the regression equation of X on Y.

Ans. The regression equation of X on Y describes the variations in the values of X for the given changes in the values ofy. In other words, this equation is used for estimating or predicting the value of X for given value of Y. This equation is expressed as follows:

When coefficient of correlation (Y) and standard deviation (6) are given in the question, it can be easily calculated as

Q.11. Define the regression equation of Y on X.

Ans. The regression equation of Y on X describes the variations in the values of Y for the given changes in the values of X. In other words, this equation is used for estimating or predicting the value of Y for given value of X. This equation is expressed as follows:

When coefficient of correlation (Y) and standard deviation (0) are given in the question, it can be easily calculated as

Q.12. Define the term regression coefficient.

Ans. Regression coefficient is an algebraic measure of the slope of the regression lines. It is for this reason that it is also known as the ‘Slope coefficient. Since, there are two regression equations, there are two regression coefficients. 

Q.13. What do you mean by partial correlation?

Ans. When we study the correlation among more than two variables, but in that study we only consider the inter-relationship between two variables and the third variable is assumed to be constant, the correlation is said to be partial correlation.

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