This document defines regression analysis and its key concepts. Regression analysis is used to estimate or predict an unknown dependent variable from known independent variables. There are two types of variables: dependent and independent. Linear regression establishes a linear relationship between a continuous dependent variable and one or more continuous or discrete independent variables using a best-fit straight line. The regression equation represents this relationship as Y=a+bX+e, where a is the intercept, b is the slope, and e is the error term, which can be used to predict the dependent variable based on the independent variables. An example is provided to predict the number of new students joining a school based on the percentage of students returning from the previous year.

1. ADMISSION
In Rathinam International Public School

2. STATISTICS
REGRESSION

3. DEFINITION OF REGRESSION
Regression analysis is a statistical device. With the help of the
regression analysis, we can estimate or predict the unknown
values of one variable from the known value of another variable.
Regression is the measure of the average relationship between
two or more variables in terms of the original units of data.

4. TYPES OF VARIABLES IN REGRESSION
ANALYSIS
There are two types of variables in regression
Independent variable (Y)
Dependent variable (X)
The variable whose value is influenced or is to be
predicted is called dependent variable, where as the variable
which influences the value or used for prediction is called
independent variable.

5. LINEAR REGRESSION
Linear regression is usually among the first few topics which
people pick while learning predictive modelling. In this
technique, the dependent variable is continuous, independent
variable(s) can be continuous or discrete and nature of
regression line is linear.
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).

6. REGRESSION EQUATION
It is represented by an equation
Y=a+b*X + e
Where,
a is intercept
b is slope of the line
e is error term.
This equation can be used to predict the value of target variable
based on given predictor variable(s).

7. PREDICTION VIA REGRESSION LINE
Number of new student (y) and Percent of previous year
student (x)
Example
Predicting number (y) of new students they join the
school based on the percent (x) of that return to the school
from the previous year.

8. THANKYOU