What is multiple linear regression analysis?
How can I easily perform this analysis with SPSS?
Multiple linear regression is an important statistical method frequently used in data analysis and forecasting. This method helps us understand how multiple independent variables are related, that is, whether they affect a dependent variable. In this article, we will focus on the basic concepts of multiple linear regression and explain when and how to use it.
What is Multiple Linear Regression?
Multiple linear regression is a statistical method used to determine whether more than one independent variable affects a dependent variable significantly (statistically significant), in other words, to model the relationship of a dependent variable with more than one independent variable.
What is a Test and When is It Used?
This method is widely used for data analysis, prediction, and understanding causal relationships. The main purpose is to explain the effects of independent variables on the dependent variable. For example, you can determine the effect of variables A, B and C on Y with multiple linear regression.
So How Can We Perform Multiple Linear Regression with SPSS?
For this, there are prerequisites that you need to consider first:
Measurement Levels: All variables must be at least equally spaced measurement level.
Linearity (Linear Relationship): Relationships between variables should be linear, that is, relationships between variables should be expressed with a straight line.
Normal Distribution: All variables must show normal distribution.
Normal Distribution of Errors: The error term ε must show a normal distribution. This increases the reliability of the predictions.
Errors should be independent of each other: Errors should be independent of each other.
Extreme Values: There should be no values considered as extreme values in the measurements.
Homoscedasticity: When there is no homoscedasticity, heteroscedasticity, that is, the variance of the error term varies, occurs. This may weaken the reliability of the model.
The conditions we have stated so far were the same as simple linear regression. In addition to these;
There should be no multicollinearity. Multicollinearity refers to a situation where there are very strong relationships between independent variables in multiple regression analysis.
In the case of multicollinearity, the relationship between independent variables is so strong that it becomes difficult to determine the isolated effect of one independent variable on the dependent variable.
This may cause the results of the analysis to be misleading. Therefore, multicollinearity is an issue that must be considered during multiple linear regression analysis. When analyzing with SPSS, it is checked according to various measures whether this problem exists.
In conclusion, multiple linear regression is a useful statistical method for data analysis and forecasting. It can be used to understand what factors have an impact on the dependent variable and to predict future values.
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