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When is the Step Deviation method used for calculating Karl Pearson's Coefficient of Correlation?
- a)When variables have large values.
- b)When variables have small values.
- c)When variables are measured nominally.
- d)When variables have a strong positive correlation.
Correct answer is option 'A'. Can you explain this answer?
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When is the Step Deviation method used for calculating Karl Pearson's ...
Introduction:
The Step Deviation method is a technique used for calculating Karl Pearson's Coefficient of Correlation. It is particularly useful when dealing with variables that have large values. Let's understand why this method is preferred in such cases.
Explanation:
The Step Deviation method involves converting the given values of variables into step deviations before calculating the correlation coefficient. This is done by subtracting a constant value (step) from each value of the variable. The step is chosen in such a way that it simplifies the calculations and reduces the range of values.
When dealing with variables that have large values, the range of values becomes significant. This can lead to difficulties in calculations and may result in inaccuracies. By converting the values into step deviations, the range of values is reduced, making the calculations more manageable and accurate.
Advantages of using the Step Deviation method for large values:
1. Simplifies calculations: The Step Deviation method reduces the range of values, making the calculations simpler and easier to perform.
2. Reduces errors: Large values can introduce errors in calculations due to their magnitude. Step deviations help in reducing these errors and improving the accuracy of the correlation coefficient.
3. Standardizes the data: Step deviations standardize the data by reducing the range of values. This allows for a better comparison and interpretation of the correlation coefficient.
Example:
Let's consider an example to illustrate the use of the Step Deviation method for variables with large values.
Suppose we have two variables, X and Y, with values ranging from 1000 to 5000. The step deviation method can be used to convert these values into step deviations by subtracting a constant step value, such as 1000.
X: 1000, 2000, 3000, 4000, 5000
Y: 1000, 2000, 3000, 4000, 5000
After subtracting the step value, the step deviations will be:
X: 0, 1000, 2000, 3000, 4000
Y: 0, 1000, 2000, 3000, 4000
These step deviations can then be used to calculate the correlation coefficient using the formula for Karl Pearson's Coefficient of Correlation.
Conclusion:
The Step Deviation method is used for calculating Karl Pearson's Coefficient of Correlation when dealing with variables that have large values. It simplifies calculations, reduces errors, and standardizes the data, making it easier to interpret the correlation coefficient. By converting the values into step deviations, the range of values is reduced, improving the accuracy and reliability of the correlation analysis.
The Step Deviation method is a technique used for calculating Karl Pearson's Coefficient of Correlation. It is particularly useful when dealing with variables that have large values. Let's understand why this method is preferred in such cases.
Explanation:
The Step Deviation method involves converting the given values of variables into step deviations before calculating the correlation coefficient. This is done by subtracting a constant value (step) from each value of the variable. The step is chosen in such a way that it simplifies the calculations and reduces the range of values.
When dealing with variables that have large values, the range of values becomes significant. This can lead to difficulties in calculations and may result in inaccuracies. By converting the values into step deviations, the range of values is reduced, making the calculations more manageable and accurate.
Advantages of using the Step Deviation method for large values:
1. Simplifies calculations: The Step Deviation method reduces the range of values, making the calculations simpler and easier to perform.
2. Reduces errors: Large values can introduce errors in calculations due to their magnitude. Step deviations help in reducing these errors and improving the accuracy of the correlation coefficient.
3. Standardizes the data: Step deviations standardize the data by reducing the range of values. This allows for a better comparison and interpretation of the correlation coefficient.
Example:
Let's consider an example to illustrate the use of the Step Deviation method for variables with large values.
Suppose we have two variables, X and Y, with values ranging from 1000 to 5000. The step deviation method can be used to convert these values into step deviations by subtracting a constant step value, such as 1000.
X: 1000, 2000, 3000, 4000, 5000
Y: 1000, 2000, 3000, 4000, 5000
After subtracting the step value, the step deviations will be:
X: 0, 1000, 2000, 3000, 4000
Y: 0, 1000, 2000, 3000, 4000
These step deviations can then be used to calculate the correlation coefficient using the formula for Karl Pearson's Coefficient of Correlation.
Conclusion:
The Step Deviation method is used for calculating Karl Pearson's Coefficient of Correlation when dealing with variables that have large values. It simplifies calculations, reduces errors, and standardizes the data, making it easier to interpret the correlation coefficient. By converting the values into step deviations, the range of values is reduced, improving the accuracy and reliability of the correlation analysis.
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When is the Step Deviation method used for calculating Karl Pearson's ...
The Step Deviation method is used for calculating Karl Pearson's Coefficient of Correlation when variables have large values. This method helps reduce the computational burden by converting the values into step deviations from assumed means, making the calculations more manageable, especially when dealing with large numbers.
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When is the Step Deviation method used for calculating Karl Pearson's Coefficient of Correlation?a)When variables have large values.b)When variables have small values.c)When variables are measured nominally.d)When variables have a strong positive correlation.Correct answer is option 'A'. Can you explain this answer?
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