Linear regression is a statistical technique used to model the relationship between variables and predict future outcomes. It fits a linear equation to observed data to represent the connection between a dependent variable and one or more independent variables. Linear regression can be used for tasks like evaluating trends, forecasting effects, analyzing the impact of changes, and assessing risks.

1. Linear Regression
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2. Regression is a statistical technique used in finance,
investing, and other fields to evaluate the degree and
nature of a connection between two or more
dependent variables.
What is Regression

3. Determining the strength of predictors.
Forecasting an effect.
Trend Forecasting.
Uses of Regression

4. By fitting a linear equation to observed data, linear
regression seeks to model the connection between two
variables. The equation for a linear regression line is Y = a +
bX, with X as the explanatory variable and Y as the
dependent variable.
Linear Regression

5. Classification and regression capabilities.
Data quality.
Computational complexity.
Comprehensible and transparent.
Linear Regression Selection Criteria

6. Evaluating trend and sale estimate.
Analyzing the impact of price changes.
Assessment of risk in financial service and
insurance domain.
Where is Linear regression used:-
Linear Regression Used

7. Linear Regression Algorithm
Understanding Linear Regression algorithm:-
Dependent
variable
Independent variable
x
y
Line

8. Y = Dependent variable
X = Independent variable
b+ = Y- intercept
b1 = slope of the line
e = errorvariable
The first order Linear model
Linear Regression
Y=b0+b1 X+e

9. If the goal is prediction, or forecasting, linear regression
can be used to fit a predictive model to an observed data
set of Y and X value.
After developing such model, if an additional value of X is
then given without its accompanying value of Y.
The Fitted Model can be used to make a prediction of the
Value of Y.
Given a variable y and a number of variable X1,.........Xn
that may be related to Y, linear regression analysis can be
between Y and the Xj.
To assess which Xi may hvae no relationship with y at all,
and to identify which subset of the Xj contain redundant
information about y.
Application of Linear Regression

10. Topics for next Post
Logistic regression
Naive bayes
Linear Discriminant Analysis
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