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.
used to predict the dependent variable
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.
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.
multivariate regression
this is for a class in Math-233-statistics
used to predict the dependent variable
Possible maybe
True.
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.
regression analysis
Regression mean squares
Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.
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.
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.
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.
multivariate regression