HR Analytics: Factors impacting appraisal (excel analytics)

Use Linear Regression Technique to Discover Important Factors affecting Salary Hike

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HR Analytics: Factors impacting appraisal (excel analytics)

What You Will Learn!

  • MS Excel
  • Data Analysis Toolpak
  • Linear regression
  • Univariate analysis in MS Excel
  • Bivariate analysis in MS Excel
  • Linear regression using Data Analysis Toolpak
  • Feature engineering in MS Excel
  • Make accurate predictions in MS Excel
  • Make powerful analysis
  • Create strong added value to your business
  • Learn to interpret output of Linear Regression in MS Excel

Description

Hello everyone, I welcome you to the course “HR Analytics : Factors affecting Performance Appraisal”.


Appraisals are an act of assessing something or someone. A performance appraisal is a regular review of an employee's job performance and overall contribution to a company. Also known as an "annual review," "performance review or evaluation," or "employee appraisal," a performance appraisal evaluates an employee’s skills, achievements and growth, or lack thereof. Companies use performance appraisal meetings to justify an employee’s reward which is usually increment in salary or bonus, based on their performance. Employees get good increments if their performance was commendable, whereas they get low increments or no increments at all if their performance was poor. The HR department is heavily engaged in it. It is clear by performance appraisal definition that this process affects the employees heavily. A good increment will lead to a more productive and satisfied employee. Whereas a low increment might lead to an unhappy employee which in turn might lead to the employee leaving the company. Hence this process should be unbiased. If an employee gets a lower increment than he deserves then he would be really unsatisfied and would look forward for an opportunity in another company.


After this course you will be able to judge whether the performance appraisal process is biased or not. There can be many reasons leading to a biased appraisal process, like experienced employees getting higher increment, employees belonging to a particular department getting higher increment, male employees getting higher increment, etc. Performance appraisal should only be dependent on an employee’s performance and not on anything else.


We will use linear regression to find out factors which influence performance appraisal. This would help us find out if the process is biased or not.


We have created a very simple structure of the course in order to understand performance appraisal, how to judge if this process is biased or not, and what are the significant factors contributing towards it. The structure of the course is as follows:

  1. Abstract and course objectives of this course.

  2. The step by step approach created by unlock Hr to solve any business problem.

  3. Understanding the business problem we need to solve in this course.

  4. Introduction and installation for MS Excel Add-in Data Analysis Toolpak.

  5. Understand how to discover and collect data.

  6. Understand how to prepare and explore the data for meaningful insights.

  7. Apply the Linear Regression technique to find significant factors contributing towards performance appraisal.

  8. To go through the major findings and insights extracted from the data.

  9. Conclusion of the course.


This course is created to understand and improve the voice of employees (VoE) who are the most crucial assets for any organization. With the help of this course, you can focus on your employees more, which in turn will be beneficial for the ROI.

Who Should Attend!

  • Managers who want to make data-driven decisions about employee, teams and their management practices.
  • HR Professionals who want to measure and improve the performance appraisal process in their organization.
  • Business owners and entrepreneurs.
  • Students or any individual who want to advance into HR or analytics.

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Tags

  • Excel Analytics
  • HR Analytics

Subscribers

224

Lectures

27

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