Regression is a statistical method used to examine the relationship between one dependent variable and one or more independent variables. The goal of regression analysis is to understand how the value of the dependent variable changes as the values of the independent variables shift.
In regression analysis, there are several assumptions that need to be met for the results to be valid. Here are the key assumptions for simple linear regression:
Linearity: The relationship between the dependent variable (Y) and independent variable (X) should be linear. This means that changes in Y are proportional to changes in X.
Independence: The observations should be independent of each other. In other words, the value of one observation should not be influenced by the value of another observation.
Homoscedasticity: Also known as constant variance, this assumption states that the variance of the residuals (the differences between the observed and predicted values) should be constant across all levels of the independent variable.
Normality of Residuals: The residuals should be normally distributed. This means that the distribution of the residuals should be approximately symmetrical around zero.
No Perfect Multicollinearity: In the case of multiple regression, there should be no exact linear relationship between the independent variables. This is known as multicollinearity.
No Autocorrelation: In time series data or panel data, there should be no correlation between the residuals at different time points or across different observations.
Violations of these assumptions can lead to biased parameter estimates, inefficient predictions, and incorrect inferences. Therefore, it's important to check these assumptions when conducting regression analysis and take appropriate steps if any assumptions are violated. Various diagnostic tests and techniques are available to assess the validity of these assumptions.
Негізгі бет Regression Analysis
Пікірлер: 1