Multiple Regression in SPSS - R Square; P-Value; ANOVA F; Beta (Part 1 of 3)
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- Опубликовано: 14 окт 2014
- This video illustrates how to perform and interpret a multiple regression statistical analysis in SPSS.
Multiple Regression
Regression
R-Squared
ANOVA table
Regression Weight
Beta Weight
Predicted Value
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Video Transcript: In this video, we'll take a look at how to run a multiple regression in SPSS. And on your screen as an example we have four variables SAT score, social support, gender, and college GPA. And in this example we're using the first three variables SAT score, social support, and gender, to predict first year college GPA. And here SAT score was taken in high school, social support is a measure of how much support a student felt that they received from others, where higher scores indicate greater support, and that was taken in the first year in college, and then gender, our dichotomous variable, where 1 is male and 2 is female, and the variable, college GPA, was the GPA after the first year in college. And in regression what we're trying to predict in this case, college GPA, is known as our criterion variable. It's also known as the dependent variable (DV). And then the variables that we're using to predict the criterion variable, SAT score, social support, and gender, those are known as are predictors or predictor variables, and we also refer to those as independent variables (IV). And those once again are SAT score, social support, and gender. Now in multiple regression you always have one criterion or dependent variable, and for it to be multiple regression you have to have two or more predictors or independent variables. if you just had one predictor or independent variable, such as SAT score, then that would be simple regression. But since we have two or more, in this case we have three once again, we're doing multiple regression. OK so to run multiple regression SPSS we want to go to Analyze, and then Regression and then go ahead and select Linear. And here we want to move college GPA to our Dependent box and then we want to select all the predictors and move those to our Independent(s) box. And then go ahead and click OK. And our output opens here and the first table, Variables Entered/Removed, this confirms that we had the variables gender, SAT score, and social support as our predictors, and then our dependent variable, or criterion variable, was college GPA, so that looks good. OK our next two tables, Model Summary and ANOVA, these two tables, they're looking at whether are predictors, once again, SAT score, social support, and gender, when those are taken together as a set or as a group, do they predict college GPA. And the Model Summary and ANOVA table are getting that slightly different things, but they're very closely related. So let's go ahead and start with Model Summary and take a look at that. So for Model Summary in this video we're going to focus on R square and then in another video we'll talk about these measures in more detail. But for this general overview the most commonly reported value in the Model Summary table is the R square value. And R squared, if I round this to two decimal places and then convert it to a percentage, so this would round two .50 or 50%, I could interpret R squared as follows. R squared once again is equal to .50 and then taken as a set the predictors SAT score, social support, and gender, account for 50% of the variance in college GPA. OK so R squared is a measure of the amount of variance in the dependent variable that the independent variables or predictors account for when taken as a group. And that's very important, it doesn't measure how much a given individual predictor accounts for, but only when we take them all as a group, this Model Summary table says overall, the regression model, which is what is referred to sometimes as a model, these three predictors predicting college GPA, that overall model accounts for 50% of the variance. Which is pretty good in practice. OK next we have our ANOVA table
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This is SOOO good ! Thank you :)
Very clean and clear explanation .
Very clear illustrations.
Very Nice Work, Thanks a lot.
Good explanations, thanks
so clear !
Thank You Sir!
Thank you so much
thank you.this is very useful
thanq
Thank u
How you measured social support in numerical values? Plz reply
Sir can you tell me how we can measure periodical changes in results scores of students due to the policy changes by government... please sir help me
Is this SAT_score is total score of subjects or it is the mean of all subjects?
Can I take mean for multiple regression.
I have total 11 factors. out of that seven factors considered as independent and 4 factors considered as dependent variables.
How should I have to do multiple regression analysis in SPSS for seven independent variables and four dependent variables
please help in solving the above problem.... .
Hi, may I asked certain questions about SPSS? If my Dependent Variable in my questionnaire are in ordinal and nominal scale, while my independent variables are in likert scale (1-5), can I use multiple linear regression? Hope you can answer it, thank you.
One of the assumptions for using multiple regression analysis is that the dependent variables are on a continuous interval or ratio scale. The independent variable can also be dichotomous. With the variables you name I would look into using an ANOVA.
I m in need of a thesis work in doing data analysis.... could anyone help me about this??
i have one independent and two dependent variables...which test should i apply?
What method did u use
What happens if the p value is not significant?
Hello
How can I contact you please I need a professional tutor to help me with some points
Arent we suppose to look at "adjusted r square" not the r square? what is the difference between these two?
The adjusted r square adjusts for sample size, which is indeed more important in multiple regression than normal regression. Generally speaking the difference between the r squared and the adjusted r squared is pretty trivial in sample sizes 100>.
how did you change it to 50% thank you ?
he was rounding
What's the statistical treatment if I have 3 DV and 3 IV? I know multiple regression is for 1 DV and many IV. Can anyone help me?
It's multivariate multiple regression analysis. Use GLM
Is .047 statistically significant? I'm no good sorting these sorts of numbers out.
nahh it should be 0.00 or lower than 0.005
Part 1 of 3? please where are the remaining parts? and how do i reach you on whatsapp please?