This document discusses different types of regression analysis. It defines regression as modeling the dependence of one variable on one or more other variables. There are two main types: simple regression, where one variable depends on one other, and multiple regression, where a variable depends on two or more others. Linear regression finds the straight line that best models the relationship between variables. It is commonly used and can help explore and understand complex data relationships to make more accurate predictions.

1. SUBMITTED TO
SIR YOUSAF GILLANI
SUBMITTED BY
MISBAH KHADIM
SAP ID
70127118
Topic
Regression

2.  The term "regression" was used by British
biometrician sir Francis Galton in the (1822-
1911), to describe a biological phenomenon.
ExampleTall children have tall parents.
 Sir Galton's work on inherited characteristics of
sweet peas led to the initial conception of
linear regression.
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3. The dependence of one variable upon variables is called Regression. “Y = a +
bx”
There are two types of Regression Variables , which are following.
1. Independent Variable i-e X
2. Dependent Variables i-e Y
 Shows cause and effect relationship .
 independent variable is the cause and dependent variable is effect.
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4. 1. Simple Regression and Multiple Regression
3. Linear Regression
Simple regression Regression is said to be Simple, if one
variable depends upon other Variable.
Examples:
1. Heater depends upon Gas
2. Motor depends upon Electricity.
regression model that estimates the relationship between one
independent variable and one dependent variable using a
straight line.
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5. Multiple Regression
 Regression is said to be Multiple, if two or more variables depends upon
other Variable.
 social and behavioral science to determine the personality variables and
social indicators which predict social adjustment.
 It is used in testing hypothesis
 Examples
 All Electronic things depends upon Electricity.
 All Vehicles depends upon Petroleum .

6. Linear regression
When the dependency of one variable upon other variable is
represented by a straight line, then Regression is called Linear
Regression.
Example:
Current is directly proportional to voltage
Curvilinear regression
Model that attempts to fit a curve as opposed to a straight line.

7.  Powerful quantitative technique that is used in every science and
every industry.
 Multiple linear regression
 Common method
 It can help you explore and understand complicated data
relationships
 Regression models can make more accurate predictions
 Estimation of unknown parameters
 Prediction of dependent variable , “ Y = a +bx”.
 Testing of hypothesiesabout α and β.
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