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Linear Regression.pptx
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- 2. Linear Regression: • Linearregression is a method used to find a straight line that best fits a set of data points. • It helps us understand and predict the relationship between two variables. • In this technique, we have one variable called the dependent variable, which we want to predict or understand better, and another variable called the independent variable, which we think influences the dependent variable.
- 3. Assumption for Linear RegressionModel: • Linear regression is a powerful tool for understanding and predicting the behavior of a variable, however, it needs to meet a few conditions in order to be accurate and dependable solutions.
- 4. 1) Linearity : Theindependent and dependent variables have a linear relationship with one another. This implies that changes in the dependent variable follow those in the independent variable(s) in a linear fashion. 2) Independence : The observations in the dataset are independent of each other. This means that the value of the dependent variable for one observation does not depend on the value of the dependent variable for another observation.
- 5. 3) Homoscedasticity: Across alllevels of the independent variable(s), the variance of the errors is constant. This indicates that the amount of the independent variable(s) has no impact on the variance of the errors. 4) Normality: The errors in the model are normally distributed. 5) No multicollinearity: There is no high correlation between the independent variables. This indicates that there is little or no correlation between the independent variables.
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