Multiple Regression in SPSS - R Square; P-Value; ANOVA F; Beta (Part 3 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.
Video Transcript: here's the results here, so SAT is significant, social support once again was significant, and then gender was not significant, as its p-value is greater than .05. Now an important point in this table here is, that if a test is significant, that means that the amount of unique variance a predictor accounts for is statistically significant. So in other words, SAT score, since it was significant, it accounts for a significant amount of unique variance in college GPA. And what we mean by unique there, is that the amount of variance that SAT score accounts for, predicts, or explains in college GPA unique to itself, is significant. Now when I say unique to itself that means that SAT score explains something in college GPA that social support and gender did not explain or didn't get at. So, in other words, SAT score explains uniquely, all to its own, a significant amount of variance in college GPA. OK since social support is significant, that also means that social support explained a significant amount of unique variance in college GPA. So that's what these tests are in the Coefficients table. And as a way to try to understand this a little bit better, suppose that SAT score and social support were perfectly correlated, just hypothetically. So if SAT score and social support were correlated perfectly, they had a correlation of 1.0. If I ran this regression analysis, and I was able to get it to run successfully, there are some problems when variables are perfectly correlated that can occur, which I'll talk about in another video, but let's assume that it ran fine everything came out. If these two variables were perfectly correlated, then the p-values for both of these would not be significant, and in fact they should be 1.0 if they are perfectly correlated. Because if SAT score and social support were perfectly correlated, then that would mean that SAT score offers nothing uniquely in terms of predicting college GPA. And social support offers nothing uniquely in terms of predicting college GPA, because whatever social support offers, SAT also offers completely. So they offer nothing uniquely if they were in fact correlated perfectly. So if a test is significant here, we know that it's offering a unique contribution to our dependent variable, or college GPA in this example. So that's important to note and it's an area that's often confused in regression. So in summary once again the Model Summary and ANOVA tables, those tell us overall did our model, with all the predictors included, what was the R-squared first of all, how much variance did it account for, that's our R-squared, and then was that variance that it accounted for statistically significant, that's the ANOVA, the p-value here. And then Coefficients once again told us, on an individual level, which if any of the predictors are statistically significant. And if a predictor is significant, recall that that means that it accounts for a unique amount of variance in the dependent or criterion variable. OK one last thing I want to talk about before closing, and that is in multiple regression, if you have a categorical variable that is dichotomous, that is, it has two categories, such as gender, it's completely fine to enter into the analysis as we did, where we just go ahead and move it over into our analysis into our Independent(s) box. If there's two categories to the variable, that's completely fine. But if you have a categorical variable that has more than two categories, such as let's say ethnicity, and it had four categories, then you can't just enter that variable into the regression analysis directly, but instead you need to re-express that variable first prior to entering it. So if I have a categorical variable that has more than two categories, I can't enter it directly into the analysis. But for all quantitative variables, or what one can think of as a continuous variable, they can always be moved in directly without a problem, it only occurs with categorical variables that have more than two levels. OK that's it for an overview of multiple regression. Thanks for watching.
R-Squared
ANOVA table
Regression Weight
Beta Weight
Predicted Value
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the way you explained it was so good. i was running mad before i found this video, now i can finish up my coursework.
This is the only resource on hierarchical regression I could find that answered ALL my questions. Thank you so much!
These 3 videos were so perfectly articulated!!! This explained everything to me, thank you!
Life SAVER!! I have a research project due in a few days and this was the clearest explanation I've seen
Excellent video. I am so grateful that you took the time to explain it so clearly!
These videos have saved my life!!! THANK YOU SO MUCH
I like this kind of explanation which explains every thing he says. That means, he is really expert not reading from a book. Well done
Cheers for the help. Decided to do reg analysis for my diss, havent done it in 3 years. Nice little recap this, genuinely helped a lot.
Your videos helped me so so much. Please keep on making more for your fans.
I want to appreciate how you made easy the regression analysis
Excellent and lucid explanation. Thank you so much for the efforts.
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Thank you so much, these three videos were so very helpful!
Thank you so much, you are a great teacher.
This has been very helpful in interpreting multiple regression analysis output
I have my psychology report due in at midnight and this analysis saved my life thank you
Thanks very much for your explanations. I could not find it elsewhere
you explained it perfectly , thank you
Dear Quantitative Specialists , Your videos are very helpful, I would like to request you to please explain the concept of durbin watson and collinearity test in Multiple Regression.
You saved my project, Thank you so much
Thanks for being a good guide.
You explained it very well...thank you very much
very very helpful. i can comfortably interpret the results.
Excellent video. My assignment will benefit from it. Thanks
So helpful! Thank you!
This provided an explanation that I couldn't find anywhere else. Thank you
very clear! great!
Really helpful, you are a great teacher! Thank you!
+AbyssWalkerX8
Thanks, AbyssWalkerX8! Best of success with your regression analyses!
QS
THANK YOU!!
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Saved my semester
Thank you so much
wow such a nice explanation
This last thing you have explained in brief: ethnicity with four cathegories, well I have exactly such a situation with the European values survey, and I do not know how to create the three different variables that collectivelly measure ethnicity. Do you have a video for that?
i wish to thank you for these wonderfully helpful videos.
you are a god
THANKS
what eye opening and informative video. how does not get or download the video to watch offline ?
Hi! thank you so much for your great explanations.
I am working on a multiple regression and some of my variables are categorical, with more than 2 labels. how can I compute them in order to include them in the analysis?
thank you in advance for your help!
Hi, Quantitative Specialists
It was really great videos,
can you please help me to understand regression graph.
specially a combined graph that shows the linearity of all predictors over (DV).
Good video, but you did not explain the equation. How you will write the relationship as per your example?
Do you explain interpretation of Beta values here? Cause I missed it then..
Hi,
Great video! In the end you mention categorical variables with more than two categories. Is that also a problem if you have "0 = no", "1=yes" and "9 = missing value"?
+Håvard Vabø
Hi Havard,
No problem at all as long as you 'tell' SPSS that 9 means missing (so it doesn't interpret it as a value of 9).
I have a question. If your data comprise numerical and % data. Should I change % into number?
Hello,
Thank you for this helpful video.
However, i have noticed that when i myself have done my regression well the sig is 0.19 but still my constant (slope) or the 'm' in (y=mx+b) is negative. So how do i interpret it?
Thank you in advance for the reply.
Kreshila Kats A negative slope means that an increase in X is associated with a decrease in Y. However, because the p value is > .05, the test is not significant, so we would assume it is just sampling error (i.e., the slope is not significantly different from zero).
Hello Sir can you add a regression vedio with the caregorical variables.
have you covered multicollinearity anywhere if yes then please shar the link
6:30 shit, I need that really quick. I made that mistake and my deadline is in a few days. Can somebody link me the video where he explains that?
I have a question: if sex effect is not significant shall we remove it from data?
Df=3, df=26 ( from where these values came)?
Why linear modelling is used here?
Hi
I need your help
Is .047 statistically significant? I'm no good sorting these sorts of numbers out.
i wish you could teach my prof
☆⌒(*＾-゜)v THX!!
Thank you so much