Linear Regression

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Linear Regression
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What is Regression
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.
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Uses of Regression
Determining the strength of predictors. Forecastin
g an effect. Trend Forecasting.
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Linear Regression
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.
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Linear Regression Selection Criteria
Classification and regression capabilities. Data
quality. Computational complexity. Comprehensibl
e and transparent.
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Linear Regression Used
Where is Linear regression used- Evaluating
trend and sale estimate. Analyzing the impact of
price changes. Assessment of risk in financial se
rvice and insurance domain.
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Linear Regression Algorithm
Understanding Linear Regression algorithm-
y
Line
Dependent variable
x Independent variable
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Linear Regression
The first order Linear model
Yb0b1 Xe
Y Dependent variable
X Independent variable
b Y- intercept b1 slope of the line e
errorvariable
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Application of Linear Regression
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.
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Topics for next Post
Logistic regression Naive bayes Linear
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