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Generally, when the dependent variable appears to be the result of more than one independent variables, a multiple regression model may be suitable. It is difficult to justify adding an additional variable, that does not significantly reduce the residual error of the fit. The setting of thresholds to justify addition of variables is in the area of "stepwise regression." The data must be adequate and consistent with the assumption of independent variables. I note from the first related link: Most authors recommend that one should have at least 10 to 20 times as many observations (cases, respondents) as one has variables, otherwise the estimates of the regression line are probably very unstable and unlikely to replicate if one were to do the study over. See related links. Many more are available in the internet. Also, many books have been written on the multiple regression- proper and improper use.

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Q: When you introduce more than one independent variable into a linear regression analysis?
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Related questions

What happens when you introduce more than one independent variable into a linear regression analysis?

this is for a class in Math-233-statistics


In regression analysis the independent variable is?

used to predict the dependent variable


is it possible to use primary data as independent variable and secondary data as independent variable in correlation analysis and regression analysis?

Possible maybe


Multiple regression analysis examines the relationship of several dependent variables on the independent variable?

True.


What is the role of the stochastic error term in regression analysis?

Regression analysis is based on the assumption that the dependent variable is distributed according some function of the independent variables together with independent identically distributed random errors. If the error terms were not stochastic then some of the properties of the regression analysis are not valid.


What is a mathematical procedure that predicts the dependent variable on the basis of knowledge known about independent variables?

regression analysis


What is the variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by?

Regression mean squares


Simple regression and multiple regression?

Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.


What does one have to do before a regression analysis?

Before undertaking regression analysis, one must decide on which variables will be analysed. Regression analysis is predicting a variable from a number of other variables.


How do I perform regression analysis in SPSS?

To perform regression analysis in SPSS: Open your dataset in SPSS. Go to "Analyze" > "Regression." Select the type of regression analysis (linear or multiple). Move the dependent variable to the "Dependent" box. Move independent variables to the "Independent(s)" box. Optionally, specify additional settings. Click "OK" to run the analysis. Interpret the results in the generated output. You can take professional help also. Experts can surely help you and assist you in performing such data analysis tasks.


What is regression analysis?

In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.


What regression method would be used when there is more than one independent variable?

multivariate regression