Regression analysis is a predictive modeling technique used to investigate relationships between variables. There are several types including linear, logistic, and polynomial regression. Linear regression establishes relationships between dependent and independent variables using a best fit line. Logistic regression is used when dependent variables are binary. Polynomial regression allows independent variables to have powers higher than one. Regression analysis can be useful for human resources forecasting, determining staffing needs based on factors like projected sales or call volumes. It indicates significant relationships between dependent targets and independent predictors.

1. REGRESSION ANALYSIS IN HR
BY:
NITYA GARG
IBS BUSINESS SCHOOL- GURGAON

2. What is Regression Analysis?
Regression analysis is a form of predictive modeling
technique which investigates the relationship between a
dependent (target) and independent variable (s)
(predictor). This technique is used for forecasting, time
series modeling and finding the causal effect relationship
between the variables. For example, relationship between
rash driving and number of road accidents by a driver is
best studied through regression.

3. TYPES OF REGRESSION ANALYSIS:
 LINEAR REGRESSION ANALYSIS
 LOGISTIC REGRESSION
 POLYNOMIAL REGRESSION

4. LINEAR REGRESSION
 Linear Regression establishes a relationship
between dependent variable (Y) and one or more
independent variables (X) using a best fit straight
line (also known as regression line).

5. LOGISTIC REGRESSION
Logistic regression is used to find the probability of
event=Success and event=Failure. We should use
logistic regression when the dependent variable is
binary (0/ 1, True/ False, Yes/ No) in nature. Here the
value of Y ranges from 0 to 1 and it can represent by
following equation:
log(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3+ b k X k

6. POLYNOMIAL REGRESSION
A regression equation is a polynomial regression
equation if the power of independent variable is
more than 1. The equation below represents a
polynomial equation:
y=a +b*x^2

7. REGRESSION IN HR DEMAND
FORECASTING
 How regression analysis is useful in human
resources demand forecasting
 Regression model would incorporate a rate of
change based on historical productivity improvement
trends.
 These models also can be used to evaluate the
required mix of the employee categories

8. CONT.
 This valuable forecasting enables us to plan and
execute recruitment, selection, training, and
development program in planed, proactive fashion
to ensure the trained marketing staff are on hand
exactly when required the organization.

9. APPLICATION OF QUANTITATIVE TECHNIQUE
 In companies, linear regression technique is used to
predict the number of employees to be recruited
each year according to the projected sales.
 In call centers, linear regression technique is used
to predict the number of employees required as per
the no. of calls that need to be responded so that
they do not lose their customers because of being
under staffed.

10. LEARNING
 How regression is used in the field of HR.
 It indicates the significant relationships between
dependent variable and independent variable.
 Scatter graphs are used to determine whether there is a
relationship between the two variales.
 In scatter graphs ,ideally all the points should on the line
and the deviation of the points from the line are the
errors.
 The process of performing regression mathematically.

11. Thank You 