Multiple linear regression in SPSS - calculation and interpretation

This tutorial shows you how to conduct a multiple linear regression in SPSS and interpret the output /regression results. 📋 Multiple linear regression basics ============================= The multiple linear regression aims on using at least two independent variables (predictors) to predict the dependent variable (outcome). For example, you want to check the influence of IQ, motivation and the GPA on income. The linear regression uses the least squares method, meaning the squared distances between the obervations and an ideal line would be minimal. Since we have multiple independent variables, you will have an n-dimensional scatter plot with a corresponding regression line - hard to imagine that, which is why we stick to numbers only. A multiple linear regresison comes with a lot of requirements, which are listed below and will receive separate videos going forward. Afterwards, you can use the Linear Regression function in SPSS and define your model and calculate it. Once calculated, you will focus on I) the F-Test, II) the model fit and III) the regression coefficients table. Interpretation of output =================== I) The F-Test basically tells you whether your model is better than no model. II) The model fit is the coefficient of determination and ranges from 0 to 1. 1 meaning you can explain 100% of the variance of your outcome variable with your model - barely possible in the real world. III) The regression coefficients have p-values, which tells you whether your independent variables have has an influence on the dependent variable or not. Then you can look at the coefficients themselves and see if they have a positive or negative influence on the dependent variable. The coefficient itself, more precise the b shown, is the change in the dependent variable if the independent variable changes by one unit. The standard errors of the coefficients measure the accuracy of the regression coefficients, representing the average distance that the observed values fall from the regression line. If you have a categorial predictor, refer to this video:    • Linear Regression with dummy variables in ...   🧾 Basic requirements of a simple linear regression ========================================== 1. linearity 2. random sample 3. exogeneity of the independent variables 4. homoscedasticity of the residuals    • How to check Homoscedasticity in Linear Re...   5. normal distribution of the residuals:    • How to Check normality assumption in simpl...   6. interval or ratio scale level of the dependent variable:    • Understanding Measurement Levels in Statis...   7. no influential cases:    • Video   8. no multicollinearity:    • 3 ways to test for multicollinearity in li...   9. no autocorrelation (optional) ⏰ Timestamps: ============== 0:00 Introduction 0:13 Calculation of the simple linear regression 1:06 Methods for entering predictors 1:51 Interpretation of the F-Test 3:23 Interpretation of Model fit 4:56 Interpretation of the Coefficients table If you have any questions or suggestions regarding the multiple linear regression in SPSS, please use the comment function. Thumbs up or down to decide if you found the video helpful. #statorials Support channel? 🙌🏼 =================== Paypal donation: https://www.paypal.com/paypalme/Bjoer... Amazon affiliate link: https://amzn.to/49BqP5H