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
From India, Mumbai
Dear Manmeet
When you conduct a Regression analysis in Compensation, you are trying to establish the correlation or closeness between two variables. Example 1 - 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); &
4. to identify gaps in grade structure.
This graph is also known as Scattergrams. The coloured points represent incumbents in the job. A job evaluation must be conducted first to determine the job size/points, followed by salary inputs before such as analysis can be done.
In this case, the 100% line, which is also known as the Mid-Point line is the company salary practice line. When this practice line is compared to the market when you participate in a salary survey, you are able to determine how well you are paying your staff and also to determine 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 right bottom of the graph. When r2 = 1, it represents a "perfect" situation (although in real life, it will never be a "1" situation). The more r2 is further from "1", it means that there are lots of "outliers" and these "outliers" causes Internal Inequity. This internal inequity needs to be urgently addressed as it affects staff morale; and impacts recruitment & retention.
Hope this is useful.
Regards
Autumn Jane
From Singapore, Singapore
137When you conduct a Regression analysis in Compensation, you are trying to establish the correlation or closeness between two variables. Example 1 - 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); &
4. to identify gaps in grade structure.
This graph is also known as Scattergrams. The coloured points represent incumbents in the job. A job evaluation must be conducted first to determine the job size/points, followed by salary inputs before such as analysis can be done.
In this case, the 100% line, which is also known as the Mid-Point line is the company salary practice line. When this practice line is compared to the market when you participate in a salary survey, you are able to determine how well you are paying your staff and also to determine 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 right bottom of the graph. When r2 = 1, it represents a "perfect" situation (although in real life, it will never be a "1" situation). The more r2 is further from "1", it means that there are lots of "outliers" and these "outliers" causes Internal Inequity. This internal inequity needs to be urgently addressed as it affects staff morale; and impacts recruitment & retention.
Hope this is useful.
Regards
Autumn Jane
From Singapore, Singapore
Hi Jane,
Many thanks for your inputs, would surely like to have some articles, updates from you end as a contribution to this cite....surely your inputs on this thread are par excellnce.
Regards
Arvind Singh
From India, Delhi
Many thanks for your inputs, would surely like to have some articles, updates from you end as a contribution to this cite....surely your inputs on this thread are par excellnce.
Regards
Arvind Singh
From India, Delhi
Dear Autum 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 "least square regression equation". 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
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 "least square regression equation". 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
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
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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