Step 10: The following are the results that you are going to generate Step 7: Got to Plot if you want to get Histogram or Scatter Plots. Step 6: Go to Statistics to select the properties you want to analyze. Step 5: Move the dependent variable and the independent variables to their respective bars by clicking on them and using the arrows by the sides of the bar to move them. Step 4: Take your cursor Regression at the dropdown navigation button for other dropdown navigation menu on Regression and select linear. Step 3: Go to analyze at the Top part of your computer in the SPSS dashboard. Step 1: Import your excel data codes into SPSS The following are the easiest guides on how run Multiple Linear Regression Analysis in SPSS. Following the guidelines that I am going to reveal in this article, you would be able do it by yourself.Īll you need to have is a laptop and the determination to work, and you would be able to run a multiple linear Regression analysis on your own. Most people find it difficult to analyze a multitude linear Regression model due to the fact that they consider it too complex a task to perform. This article specially covers the step by step guide on how multiple linear Regression model can be analyzed. SPSS is a statistical tool that can be used to run the regression analysis. How to Analyze Multiple Linear Regression Model. To know the effects of the factors above on cassava output, analysis has to be performed based on the data collected from the variables. The Regression equation presented above shows that the quantity of cassava output produced by the farmers in the study area depends on the Age distribution of farmers, Farming Experience, Source of Fund and Farm sizes. The linear regression model equation for this example can deduced below It means that the dependent variable is Y (cassava output), whereas L (representing Age of farmers (L 1), Farming Experience (L 2), Source of Fund (L 3) and Farm sizes (L 4)) are the independent variables. Here, cassava output can be represented as Y, while the selected factors as L. That is, the dependent variable changes as the independent variables are changing.įor example, let us consider the Determinants of cassava output in a particular study area using some selected factors like Age of farmers, Farming Experience, Source of Fund and Farm sizes. In any regression analysis, there are always one or more independent variables.Īs explained above, changes in the independent variables determine what happens to the dependent variable. Independent variables are otherwise called explanatory variables, predictor variables, factors or the regressors. This implies that whatever happens to the dependent variable must be determined by the independent variables according to the relationship between them. The mean value of the dependent variable is subject to those of the independent variables. It is the variable whose fate is determined by the changes of the explanatory variables. There is always one dependent variable in Regression analysis. The dependent variable is also known as the outcome variable, response variable or the regressand. Example of this are: Ridge regression, Lasso regression, Polynomial regression and Logistic regression Non-linear Regression: This type of regression analyses does not show a linear relationship. Multiple linear Regressions: When the relationship is in between the dependent variable and two or more independent variables. Simple linear regression: This is when you are considering the relationship between the dependent variable and an independent variable. Regression analysis is divided into the following The relationship that is seen in regression model are usually between the dependent variable and one or more independent variables. It is a model for checking the effects of set of selected factors on the subject matter being discussed or analyzed. Regression analysis is one of the Statistical models for estimating the relationship between variables.