SPSS On-Line Training Workshop |
In this Tutorial: |
Back to Statistical Procedures
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In this on-line workshop, you will find many movie clips. Each movie clip will demonstrate some specific usage of SPSS.
Linear regression: This helps us analyze the variables to set up a model of the data in the form of an equation.
Method: This lets you select how independent variables are entered into the analysis. The "Enter" method enters all variables at the same time. The other methods involve some sort of step-wise regression. | |
Statistics: To check for collinearity problems, we need to make sure that Estimates and Model fit are selected. (These are usually selected by default.) R-squared change is needed for variable selection methods. If you want to check for collinearity problems, you can select "Collinearity diagnostics". You can make other selections, if needed. | |
Plots: You have the option of plotting the residuals. | |
Save: You can save some of the results of the analysis, either to the data editor or to a new file. | |
Options: If you are doing stepwise regression, you can change the criteria for entry and removal under this submenu. |
click
here to watch Linear Regression
click
here to watch Stepwise Regression
Binary Logistic and Multinomial Logistic Regression: This is used to determine factors that affect the presence or absence of a characteristic when the dependent variable has two levels (binary logistic) or more (multinomial logistic).
Method: This lets you select how independent variables are entered into the analysis. The "Enter" method enters all variables at the same time. The other methods involve some sort of step-wise regression. | |
Select: This button is used if you want to limit your analysis. | |
Categorical: Here is where you identify categorical variables and specify how you want this data compared. | |
Save: This allow you to save output as new variables in the data editor window. | |
Options: If you are doing stepwise regression, this is where you can set your entry and removal criteria. Another option that can be checked here is the "CI for exp(B):". This gives you the odds ratio and is helpful in interpretation of parameter estimates. |
click
here to watch Logistic Regression
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©1999 Dr Carl Lee, Dr Felix Famoye, Central Michigan University. Joyce Sharp, student assistant. All rights reserved.