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In calculating the Karl Pearson’s coefficient of correlation it is necessary that the data should be of numerical measurements. The statement is
- a)Valid
- b)Not valid
- c)Both
- d)None
Correct answer is option 'A'. Can you explain this answer?
Most Upvoted Answer
In calculating the Karl Pearson’s coefficient of correlation it ...
The Karl Pearson correlation coefficient, also known as the Pearson correlation coefficient, is a measure of the linear relationship between two variables. It is denoted by the symbol "r" and ranges from -1 to 1.
To calculate the Pearson correlation coefficient, follow these steps:
1. Collect data: Obtain a set of paired values for the two variables you want to analyze. For example, you might have data on the height and weight of individuals.
2. Calculate the means: Find the mean (average) of each variable. This involves summing up all the values and dividing by the total number of observations.
3. Calculate the deviations: For each pair of values, subtract the mean of each variable from its respective value. These are the deviations from the means.
4. Calculate the squared deviations: Square each deviation calculated in step 3.
5. Calculate the product of the deviations: Multiply the deviations of the two variables for each pair of values.
6. Calculate the sum of the squared deviations: Add up all the squared deviations calculated in step 4.
7. Calculate the sum of the product of the deviations: Add up all the products of the deviations calculated in step 5.
8. Calculate the Pearson correlation coefficient: Divide the sum of the product of the deviations (step 7) by the square root of the product of the sum of the squared deviations (step 6) for each variable.
9. Interpret the coefficient: The resulting value of "r" will range from -1 to 1. A positive value indicates a positive linear relationship, while a negative value indicates a negative linear relationship. A value of 0 indicates no linear relationship.
Note: It is important to remember that the Pearson correlation coefficient only measures linear relationships and assumes that the data follows a normal distribution. If the relationship between the variables is not linear or the data is not normally distributed, alternative correlation measures may be more appropriate.
To calculate the Pearson correlation coefficient, follow these steps:
1. Collect data: Obtain a set of paired values for the two variables you want to analyze. For example, you might have data on the height and weight of individuals.
2. Calculate the means: Find the mean (average) of each variable. This involves summing up all the values and dividing by the total number of observations.
3. Calculate the deviations: For each pair of values, subtract the mean of each variable from its respective value. These are the deviations from the means.
4. Calculate the squared deviations: Square each deviation calculated in step 3.
5. Calculate the product of the deviations: Multiply the deviations of the two variables for each pair of values.
6. Calculate the sum of the squared deviations: Add up all the squared deviations calculated in step 4.
7. Calculate the sum of the product of the deviations: Add up all the products of the deviations calculated in step 5.
8. Calculate the Pearson correlation coefficient: Divide the sum of the product of the deviations (step 7) by the square root of the product of the sum of the squared deviations (step 6) for each variable.
9. Interpret the coefficient: The resulting value of "r" will range from -1 to 1. A positive value indicates a positive linear relationship, while a negative value indicates a negative linear relationship. A value of 0 indicates no linear relationship.
Note: It is important to remember that the Pearson correlation coefficient only measures linear relationships and assumes that the data follows a normal distribution. If the relationship between the variables is not linear or the data is not normally distributed, alternative correlation measures may be more appropriate.
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Community Answer
In calculating the Karl Pearson’s coefficient of correlation it ...
The Karl Pearson correlation coefficient (also known as Pearson's correlation coefficient or simply Pearson's r) is a measure of the strength and direction of the linear relationship between two continuous variables. It is denoted by the symbol "r."
To calculate Pearson's correlation coefficient, follow these steps:
1. Gather your data: You will need pairs of values for two continuous variables. Make sure the data is numerical and has a linear relationship.
2. Calculate the means: Find the mean (average) of both variables.
3. Calculate the deviations: Subtract the mean from each value for both variables. These are the deviations from the mean.
4. Calculate the products of deviations: Multiply the deviations of the two variables for each pair of values.
5. Calculate the sum of the products of deviations: Add up all the products of deviations from step 4.
6. Calculate the standard deviation: Find the standard deviation for each variable by taking the square root of the sum of the squared deviations from the mean.
7. Calculate the product of the standard deviations: Multiply the standard deviations of the two variables.
8. Calculate the correlation coefficient: Divide the sum of the products of deviations (from step 5) by the product of the standard deviations (from step 7).
9. Interpret the correlation coefficient: The resulting value will be between -1 and 1. A positive value indicates a positive linear relationship, a negative value indicates a negative linear relationship, and the magnitude (closer to 1) indicates the strength of the relationship.
It is important to note that the Pearson correlation coefficient only measures linear relationships and assumes that the variables are normally distributed. It may not be appropriate for all types of data or relationships.
To calculate Pearson's correlation coefficient, follow these steps:
1. Gather your data: You will need pairs of values for two continuous variables. Make sure the data is numerical and has a linear relationship.
2. Calculate the means: Find the mean (average) of both variables.
3. Calculate the deviations: Subtract the mean from each value for both variables. These are the deviations from the mean.
4. Calculate the products of deviations: Multiply the deviations of the two variables for each pair of values.
5. Calculate the sum of the products of deviations: Add up all the products of deviations from step 4.
6. Calculate the standard deviation: Find the standard deviation for each variable by taking the square root of the sum of the squared deviations from the mean.
7. Calculate the product of the standard deviations: Multiply the standard deviations of the two variables.
8. Calculate the correlation coefficient: Divide the sum of the products of deviations (from step 5) by the product of the standard deviations (from step 7).
9. Interpret the correlation coefficient: The resulting value will be between -1 and 1. A positive value indicates a positive linear relationship, a negative value indicates a negative linear relationship, and the magnitude (closer to 1) indicates the strength of the relationship.
It is important to note that the Pearson correlation coefficient only measures linear relationships and assumes that the variables are normally distributed. It may not be appropriate for all types of data or relationships.
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In calculating the Karl Pearson’s coefficient of correlation it is necessary that the data should be of numerical measurements. The statement isa)Validb)Not validc)Bothd)NoneCorrect answer is option 'A'. Can you explain this answer?
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