Liner Regression
Linear regression is a statistical method used to model the relationship between a dependent variable (also called the response variable) and one or more independent variables (also called predictor variables or features). The goal is to find the best-fitting line (known as the regression line) that describes the relationship between the variables.
The equation for a simple linear regression model (where there is only one independent variable) is:
ŷ = b0 + b1 * x
where:
- ŷ is the predicted value of the dependent variable (the response)
- x is the value of the independent variable (the predictor)
- b0 is the intercept (the value of ŷ when x = 0)
- b1 is the coefficient (the change in ŷ for a unit change in x)
Let's say you want to model the relationship between the number of hours studied and the corresponding test score. The dependent variable is the test score (ŷ), the independent variable is the number of hours studied (x), and b0 and b1 are the intercept and coefficient, respectively.
Example: Suppose you have data for 5 students who studied for different number of hours and got the corresponding test scores:
You can use linear regression to find the best-fitting line (the regression line) that models the relationship between the hours studied and the test score. The regression line can be used to predict the test score for a given number of hours studied.
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