Multiple regression analysis is useful for the researchers to assess the strength of the relationship between an outcome (the dependent variable) and several predictor variables which are considered as independent variables. The importance of each of the predictors to the relationship often with the effect of other predictors is statistically eliminated with error, and on the other hand, the researchers can identify the impacts of several independent variables on the dependent one.
For example, if the researchers conduct a multiple regression where they try to predict blood pressure that is considered to be the dependent variable from the independent variables such as height, weight, age, and hours of exercise per week. They can also include gender as one of your independent variables.
For regression analysis, the formula is, Y = B1X1 + B2X2 + … + BnXn + C
Hence, it is possible to demonstrate the dependent variable by the inclusion of several independent variables, which affected the dependent variable in the data set. The sign on the coefficient analysis (positive or negative) gives the direction of the effect, such that if the coefficient is positive, it means there is a positive correlation analysisbetween the dependent and the independent variables. If the coefficient becomes negative, the increase in the independent variable will decrease the dependent one. Hence, there is more than one factor in the data which are independent variables and affect the dependent variable in the data set. For analysing the interrelationship between the two or more independent variables and one dependent variable, the researchers try to utilise a multiple regression model, where the inclusion of all the independent variable further help to explore an exact relationship with its dependent variable.
Multiple regression analysis is hereby beneficial for the researchers and data analysts to assess the strength of the relationship between a dependent variable and several predictor variables or independent variables. It hereby helps them to understand the relationships among the variables presented in the dataset. The influence of all variables can be interpreted through this multiple regression model. Multiple R actually can be viewed as the correlation between response and the fitted values. In an Excel spreadsheet as well as SPSS, the researchers can conduct a ,multilane regression analysis, where R square is being positive as it is a square value. The dependent variable should be measured on a continuous scale either an interval or ratio. Two or more independent variables are there in the data set to run the multiple regression models. In SPSS, after data sorting, the regression analysis must be done by putting the dependent and independent variable names in the dialogue box. It is also important to set a confidence interval at 95% to explore the value of r. R-squared hereby measures the strength of the relationship between your model and the dependent variable on a convenient 0 – 100% scale. For example, an r-squared of 60% reveals that 60% of the data fit the regression model and there is a correlation between the dependent and significant independent variables.
It is hereby beneficial for the researchers to conduct Multiple Regressions analyses, to progress in the study by considering several variables in the model. It is one of the important statistical tools for regression analysis and identifies the influence of diverse variables that affect the dependent variable in the data set. The researchers must consider the two or more independent variables to analyse their influence on the dependent variable by exploring R values.
If you are unsure how to interpret regression equations or how to use them, you can further read about them in our enhanced multiple regression guide. Our SPSS Experts also help you in writing up the results from your assumptions tests and multiple regression output for your dissertation/thesis research, or research report.