Answer To: Assessment 3: HR Policy Recommendation Project The HR Policy Recommendation Project is an extension...
Sanjeev answered on Apr 09 2021
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Contents
Introduction: 3
Background: 3
Regression 3
Difference between regression and correlation 3
Regression Analysis 4
Metrics included in the regression analysis: 4
Analysis of report so generated 5
Recommendation on staffing policy: 5
Conclusion: 5
References 6
Appendix: 6
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Introduction:
In this assignment, regression analysis has been made in order to find out the relationship between different HR metrics and thus enable the management to draft a proper staffing policy which would not only increases the profitability and productivity of the business but also provide a clear insight into different HR metrics which would help the company in analysing the impact of financial as well as non-financial variables. It helps the company to find out the difference between actual and predicted figures and thus help to analyse the reasons of prediction error. It helps the company in making an optimum allocation of human resources within the organisation and thus able to control costs in a phased manner without compromising with the reputation of organization.
Background:
Regression
It is a method of sorting and arrangement of variables which have an impact on other parameters. It is of utmost importance for an organisation as it helps in determining the factors which have the most significant impact upon the performance of the company. It helps the company in establishing a relationship between two or more variables. There are two kinds of variables involved in regression analysis i.e., dependent and independent variables. It is a statistical method in which the impact of independent variables on dependent variable have been analysed. Under this analysis, there is only one dependent variables and unlimited independent variables to find out the relationship between one dependent variables and unlimited independent variables. There are five applications of regression analysis are as follows:
Predictive analysis: It helps in forecasting the future growth opportunities and potential threats. It helps in making future trend line i.e., either company will be more competitive or shut down its scale of operations.
Operational efficiency: It helps the company in making optimum utilisation of existing resources by defining a relationship between different variables and thus helps in optimising the business operations. It provides an insight regarding how a task is to be performed to save efforts, cost, time and ultimately end up result in achieving high profit margin.
Supporting decision: It helps the managerial personnel by providing relevant information about the different variables and thus helps in taking an informed decision which is in the best interest of organisation. Regression report provides a lot of information about the different parameters which have an impact upon the business operations.
Correcting errors: It helps in analysing prediction errors and thereby finding out the cause-and-effect relationship for such errors. Various reasons for such errors are finding out and then appropriate measures are taken against it in order to correct such errors.
New insights: It provides new insights about the industry trends i.e., growth opportunities and potential threats in which the company is operating and thus make the company capable in taking appropriate proactive measures to safeguard the company from changes that are going to arise in the near future.
Difference between regression and correlation
There are many differences betw4een correlation and regression which are given as below:
Correlation establishes a relationship that indicates how variables change in affect of some external factors while under regression, a relationship is established which states how an independent variable can cause a change in other dependent variables.
2.. Under correlation, all the variables move together while under regression, cause and effect relationship is established.
Under correlation, variables are interchangeable while under regression, variables are not interchangeable as one is dependent variable and other is independent variable.
Correlation is a single statistic whereas regression produces an entire equation. Thus, correlation is represented by a single point whereas regression is represented by a line.
Regression Analysis
Metrics included in the regression analysis:
In applying regression analysis, various HR metrics have been identified by management in order to draft an appropriate HR policy within the organisation. The company is making use of various HR metrics in order to find whether adequate human resource personnel have been appointed by the organisation and salary provision made by company is competitive or not. The HR metrics which have been used by the company to carry out regression analysis are given as below:
Work experience: It means the number of years for which the employees has actually worked within the industry. It has a direct relationship with the salary income as more work experience means more exposure and more ability to deal with the complex issues of the organisation. Employees with high work experience should be appointed with high remuneration as they would be proved to be fruitful for the organisation.
Performance90: Performance appraisal scores 90 days after joining the organisation. It ranks from 1 to 9. A higher score indicates better performance. The company should link remuneration of human resource personnel with the performance of employees and thus the employee with high performance should be remunerated with high salary and more efforts should be made towards in order to sharpen their skills more precisely.
Speed to competency: It indicates the speed of the employee in learning any skills to increase efficiency and effectiveness of performing the task. The employees who are faster in learning any skills should be compensated with high remuneration in comparison to the employees who have relatively slow speed to competency as they won’t be highly efficient for the organisation in order to achieve the ultimate results. Thus, such HR metrics should be included in order to find out whether organisation is paying excess salary to employees or non-competitive salary.
Productivity: It indicates the ability of the employees to work in favour of the organisation by contributing the most out of their skills, experience, expertise and knowledge. If the employees are highly efficient and skilled, then the company would be highly benefitted from large production of outputs from the existing resources. A employee who are highly productive for the organisation, then they should be remunerated accordingly and thus build a strong and efficient workforce. Due to large production, the company would reach achieve economies of scale, average cost per output would be less and profit margin would be high. As a result, overall profitability would be ultimately increased and helps the organisation in retaining its market position for a long period of time.
Bradford factor: Bradford factor is a HR metrics which is used to measure human resource personnel absenteeism. It determines the impact of employee’s absenteeism upon the performance of company. A short and planned absenteeism would not affect the performance of company to a larger extent in comparison to unplanned and non-continuous leave. An employee who takes unpanned leave would be penalised for such action and thus company make such rules and regulations which reduces the level of unplanned and non-continuous leave so as to optimise the performance of the company.
Profitability: Profitability per employee can be defined as the amount of revenue which is earned by company from each employee during a specified period. A company should provide adequate and competitive salary to highly profitable employees in order to retain them for a longer period of time. This would, in result, enhances the revenue generating capacity of the organisation by making an optimal utilisation of existing resources through the deployment of skilled and experienced human resource personnel to different parts of organisation.
Analysis of report so generated
Regression analysis has been carried out to establish a relationship between salary paid to employees as a dependent variables and many other HR metrics as independent variables. Regression report has been prepared using excel software and attached in the appendix for the kind reference. Such analysis is made to see if HR policy is adequate and efficient for the organisation or not. Independent variables that have been taken into consideration for evaluation of efficiency of HR policy are work experience, performance90, speed to competency, productivity, Bradford factor, profitability. Now, brief discussion will be made regarding the interpretation of regression results.
Regression outputs:
Multiple R: This is a statistics measure which tells about how the strong linear relationships exists among different parameters. It is also known as correlation coefficient. It can also be calculated with the square root of r squared. If the multiple r is 1, it means there is a perfect linear relationship among different variables and If the multiple r is 0, it means there is no linear relationship among different variables. In our case, it is 0.330664 which indicates there is no strong relationship between salary and other HR metrics.
R squared: It is a statistics measure which tells the no. of points that fall on the regression line which is obtained by the linear relationship equation as discussed in multiple r. It is also known as coefficient of determination. In our case, it is 11.6052% which indicates that only 11.6052% of the variation of y-values i.e., dependent variable (salary) is explained by x values i.e., independent variables (HR metrics).
Adjusted r squared: It is a statistics measure which is used when there is more than one independent variable present in the regression analysis. It takes into considerations the effect of number of terms that is existed in a model. In our case, there are 6 independent variables and thus it is advisable to make the use of adjusted squared r in place of squared r in order to do the interpretation of regression reports. Hence, it can be said that only 7.5566% of the variation of y-values i.e., dependent variable (salary) is explained by x values i.e., independent variables (HR metrics) by taking into considerations of the no. of terms existed in the model.
Calculation of predicted variables:
With salary as dependent variables and other HR metrics mentioned above as independent variables, predicted figures for salary can be computed by applying below equation:
Predicted salary = Intercept + (Coefficient of work experience*Work experience) + + (Coefficient of performance90*Performance90)+ (Coefficient of speed to competency*Speed to competency) + (Coefficient of productivity*Productivity) + (Coefficient of Bradford factor*Bradford factor) + (Coefficient of profitability*Profitability).
Using such equation, the company has to calculate the predicted salary for each employee and compare such figure with the actual...