Hi, I’m new in the space of Compensation. Can anyone help me to understand as to What is Regression/ Correlation in Compensation terminology. Regards, Manmeet
From India, Mumbai
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From India, Mumbai
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Dear Manmeet,
When you conduct a regression analysis in compensation, you are trying to establish the correlation or closeness between two variables. For example, age and salary, tenure and salary, job size and salary, etc.
I have attached a regression sample for job size and salary using Excel for your reference. Such analysis is used:
1. To determine internal equity of the company, i.e., the bigger the job, the higher the salary;
2. To determine the salary spread of jobs within the same job points/grade;
3. To identify outliers, i.e., jobs falling outside the two controlling lines (maximum and minimum); and
4. To identify gaps in grade structure.
This graph is also known as scattergrams. The colored points represent the job incumbents. A job evaluation must be conducted first to determine the job size/points, followed by salary inputs before such analysis can be done.
In this case, the 100% line, also known as the mid-point line, represents the company's salary practice line. When this practice line is compared to the market through participation in a salary survey, you can determine how well you are paying your staff and also identify the gap between your pay practice (where you are today) and pay policy (where you want to be).
The least square regression equation is indicated at the bottom right of the graph. When r2 = 1, it represents a "perfect" situation (although in real life, it will never be a "1" situation). The further r2 is from "1", the more "outliers" there are, causing internal inequity. This internal inequity needs urgent attention as it affects staff morale and impacts recruitment and retention.
Hope this is useful.
Regards, Autumn Jane
From Singapore, Singapore
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140When you conduct a regression analysis in compensation, you are trying to establish the correlation or closeness between two variables. For example, age and salary, tenure and salary, job size and salary, etc.
I have attached a regression sample for job size and salary using Excel for your reference. Such analysis is used:
1. To determine internal equity of the company, i.e., the bigger the job, the higher the salary;
2. To determine the salary spread of jobs within the same job points/grade;
3. To identify outliers, i.e., jobs falling outside the two controlling lines (maximum and minimum); and
4. To identify gaps in grade structure.
This graph is also known as scattergrams. The colored points represent the job incumbents. A job evaluation must be conducted first to determine the job size/points, followed by salary inputs before such analysis can be done.
In this case, the 100% line, also known as the mid-point line, represents the company's salary practice line. When this practice line is compared to the market through participation in a salary survey, you can determine how well you are paying your staff and also identify the gap between your pay practice (where you are today) and pay policy (where you want to be).
The least square regression equation is indicated at the bottom right of the graph. When r2 = 1, it represents a "perfect" situation (although in real life, it will never be a "1" situation). The further r2 is from "1", the more "outliers" there are, causing internal inequity. This internal inequity needs urgent attention as it affects staff morale and impacts recruitment and retention.
Hope this is useful.
Regards, Autumn Jane
From Singapore, Singapore
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Hi Jane,
Many thanks for your inputs. I would surely like to have some articles and updates from your end as a contribution to this site. Surely, your inputs on this thread are par excellence.
Regards,
Arvind Singh
From India, Delhi
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Many thanks for your inputs. I would surely like to have some articles and updates from your end as a contribution to this site. Surely, your inputs on this thread are par excellence.
Regards,
Arvind Singh
From India, Delhi
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Dear Autumn Jane,
Thank you for sharing this. You have explained the whole concept in a clear and crisp manner. Although I have got the gist of it, I would like to know what exactly is the "least square regression equation." Is it the relation between X and Y? What is the formula for R2 (R square)?
Once again, thank you very much for sharing this.
Regards, Ritesh
From India, Pune
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Thank you for sharing this. You have explained the whole concept in a clear and crisp manner. Although I have got the gist of it, I would like to know what exactly is the "least square regression equation." Is it the relation between X and Y? What is the formula for R2 (R square)?
Once again, thank you very much for sharing this.
Regards, Ritesh
From India, Pune
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Dear Ritesh,
Yes, you are right. Regression is a statistical technique that shows the relationship between two variables - X and Y, or in this case, Job Points and Salary, all represented as a straight line. The Linear Equation is represented by the following:
Linear Equation: Y = ax + b
where Y = $ (salary) a = slope x = Job Points b = intercept / constant
This Linear Equation can be found on the bottom right side of the graph. Hope I have answered your questions.
Regards, Autumn Jane
From Singapore, Singapore
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Yes, you are right. Regression is a statistical technique that shows the relationship between two variables - X and Y, or in this case, Job Points and Salary, all represented as a straight line. The Linear Equation is represented by the following:
Linear Equation: Y = ax + b
where Y = $ (salary) a = slope x = Job Points b = intercept / constant
This Linear Equation can be found on the bottom right side of the graph. Hope I have answered your questions.
Regards, Autumn Jane
From Singapore, Singapore
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