Thanks a lot Phil for this video. It is very helpful, instructional and detailed. It's all we need as a starter in multiple regression and for understanding the important basics.
I logged in just to tell you this: By far the simplest and clearest explanation I've had on this. Why oh why do lecturers attempt to 'pomp' up these matters. Doesn't science teach us about parsimony!? Anyway...much appreciated. You might think that it's a simple youtube video, commented on by some random viewer, but (not hyperbole) you changed my life today. Thank you. Thank you. Thank you.
By far, this is the best video I have ever gone through with regard to linear regression. Awesome presentation and right to the point. In deed helpful for people at any stage of their learning, of SPSS statistical methods. !
Glad it was helpful. I think the question you ask is answered in this other video of mine called "SPSS for newbies: Why having a high R-squared in regression could be a bad thing "
hey Phil, to get rid of the yellow box that appears every time you hoover your mouse over the tables, you can copy the table to a word document and explain the tables there instead of SPSS interface. :)
hey phil, first of all, big big thanks for ur great videos of spss.. what are the explanations between simple linear regression and multiple linear regression? could u pls give some examples? do reply plzzz.. know my questions are idiotic, but i am really now out of help now.. u'll never imagine how excited i m now running into your videos. really life saver..(thumb up)
It was good. I would like to know if the R found in the example is enough to consider the model as strong, and what are de intervale to decide about that.
Hi. If you are a newbie/taking an intro course in modelling, then you are likely to be taught that since the coeff is not significant, you may try removing it an refitting. In reality, it's not so straight forward as the choice depends on numerous things like: reason for building a model, results of other (diagnostic) tests. But you can ignore my last comment if you are a newbie!
Hi Phil, thank you so much for this video, it really helps a lot. I just have one question, at the end of the video you say that the F and T statistics are valid if certain conditions hold for the model. Can you please list these conditions or point me towards material teaching about these conditions. Or maybe you have another video where you talk about this conditions. Thanks
+Leo Christian Navarra Leo - the standardized coeff allows you to assess the relative importance of each IV to the others. To rank the importance of the Xs, drop any minus symbol in front of the coeff then compare the numbers.
Phil Chan, i don't know if you are still using this youtube account but if anyone can help me:I have found a significant value in the ANOVA table (0.024) but not any significant values in the coefficients table (0.054 and 0.132) what does this say?
+Phil Chan what does it mean if the constant in the coefficient table is non significant, but everything else in that table is significant? How do I interpret that?
+Klaudia C Klaudia - simple answer is to keep it in the model and ignore it. After all, the purpose of a regression model is for prediction (of the dependent variable) or interpretation of the coefficients on the predictors (Xs). For both, we don't have to be concerned with the intercept (the "constant" in SPSS).
Hi Phil, Thank you for this video! It is very helpful. What does the (Constant) represent? I am currently running a linear regression model in which other independent variables (including the total model) is significant, however the (constant) is not.
+bucko buckic A situation where the F-test for H0: model has no explanation power is not rejected, while some coeff in the model are individually sig is a contradiction, and so why could this be the case, and does it matter are 2 natural questions. 1) F and t tests are based on the standard assumptions of regression, which if not satisfied puts doubt in the outcome of the tests. Whether or not it even matters depends on whether you are building a model for prediction, or causal type model. For prediction one is not interested in interpreting the coeffs, and a different approach and focus on model building to one for causal type model.
Sir thank you! I have a question...if the whole model is rejected (ANOVA) considering the P-value then subsequent coefficients are also useless to be explained. My understanding we will explain and discuss about coefficients only when model is found to be fit with explanatory power... Kindly discuss it
The null hypothesis in F-test in the Anova table is that the model has no explanatory power for Y. If you reject this, and generally one does reject it, then you may have a look at the coefficients. Note, one should test the assumptions of regression (NICE-L assumption) before looking at the F and t tests.
By the way you phrase your question, I wonder whether you understand the F-test. When you say the overall model is rejected, I take this to mean you reject to null of this F-test, and this means there is evidence the model has explanatory power. BUT perhaps you mean to say that suppose we DO NOT reject the F-test ie no evidence the model has explanatory power, then do the coeff have meaning. Supposing all the assumptions underpinning the model are satisfied then if there is no evidence the model has explanatory power those coeff don't tell us anything. Good luck.
Hello, This video is helpful, one more question: What is the meaning of the p value of the constant (0.029) in the last table? What if its > 0.05? it means the model is not significant or what? because I have two IVs, one is significant (.001) and another is not significant (.689) and the p value of the top column is .332, what is it stand for? Thanks
That's for the intercept. p-value for intercept >0.05 suggests it's not significant, but do not delete it. In applications, the importance on the slope not the intercept.
Thanks for the video and guidance! But I have a question: I did a multiple regression analysis and the results showed that there were significant correlation between the DV and IVs, however in the coefficient table certain IVs had p-value higher than 0.05. How do I interpret this?
If I understand correctly, you looked at correlations between DV and IVs and found that some were significant, but then when you put them in the regression the coeff was insignificant. This is common place when running multiple regression. From a newbie perspective this could be because of multicollinearity problem (I have a video explaining this). Other reasons that could explain what you see in your analysis are mediation, and confounding variables. These last 2 need another video, which I have in the pipeline.
Thank you so much for this! If my coefficient is very small (-0.004), how can I interpret that or how should I move the decimal? I hope this makes sense...
Supposing you are talking about a slope parameter Think about the units of measurement your x that has the small param. A rescaling of the units of X would change the param. eg if X were measured in cents then changing units to 1000 dollars would change it to -4 from -0.004.
This was the best explanation I have received all year, you just simplified everythin... Thank you
I love watching your videos in my free time!
you may have just helped me write my analysis for my dissertation, thank you.
You are a life saver...you have practically led me by the hand through my quantitative data analysis as a first timer. Thanks so much
Thanks a lot Phil for this video. It is very helpful, instructional and detailed. It's all we need as a starter in multiple regression and for understanding the important basics.
U r the only person who has made this make sense for me. Thank u.
I logged in just to tell you this: By far the simplest and clearest explanation I've had on this. Why oh why do lecturers attempt to 'pomp' up these matters. Doesn't science teach us about parsimony!? Anyway...much appreciated. You might think that it's a simple youtube video, commented on by some random viewer, but (not hyperbole) you changed my life today. Thank you. Thank you. Thank you.
Such a kind comment. Thank you. I watched the video to see what was good about it.
Thank you so much! You have no idea how much trouble this has saved me.
12:51 minute video on three months of Econometrics classes.. In Layman's terms no less.. I salute you sir :)
By far, this is the best video I have ever gone through with regard to linear regression. Awesome presentation and right to the point. In deed helpful for people at any stage of their learning, of SPSS statistical methods. !
you have saved my masters research!!! thank you so much
Op explanation bro thanks , 👍👍👍👍
Keep it up
You may just have helped me complete my dissertation study, thank you kind sir.
Glad it was helpful. I think the question you ask is answered in this other video of mine called "SPSS for newbies: Why having a high R-squared in regression could be a bad thing "
That is great. Is very simple. Good job.
thank you so much! you save my life and my report!
SAME!!!!!!
GENIUS! thank youuuu you saved my thesis
+wonderfulworldofdee same here :) - thanks phil!
you are a wonderful teacher
That was very well explained and easily understood
Thanks
Thank You! You make it so much easier to understand spss :)
Thank you so much! You are my new Jesus
this really has saved me..thanks
hey Phil, to get rid of the yellow box that appears every time you hoover your mouse over the tables, you can copy the table to a word document and explain the tables there instead of SPSS interface. :)
Yeah, those pop up boxes get in the way.
This is very educative.Thank you very much
great described in a simple manner. Thankyou
Excellent insights shared using a simple example.
Wonderful concise explanation. Excellent!! Cant thank you enough
This was so helpful! Thanks a million, Phil!
Is it term time for you? It's August and all my students are on holiday till October
We're actually now wrapping up. The exam was yesterday. My classes are delivered in trimesters (May-August, Sept-Dec, Jan-Apr)
Another viewer from South Africa is in the middle of a term.
Thank you. Really great explanation! Good refresher, now I can write up my data section this weekend.
Great video, it helped a lot preparing for my stats exam
Nice job Phil.
Great video, helping me so much with my dissertation!
Thumbs Up! explains all I needed
Every word from every minute of every second of this video is sooo fucking important! Ur so awesome
Thank you so much!! This is easy to understand. You are an awesome teacher. :)))
Really really great Video! Thanks!
Thank youso much.. It really helps and clarified lot of confusions.
Thanks a lot.You are great.
oh thank you! you are my savior!
Loved ma man getting peeved at dah yellow box tings
.....pow pow pow
very great video, appreciate your time
thank you very much sir i really thank you!!..keep in touch!
hey phil, first of all, big big thanks for ur great videos of spss.. what are the explanations between simple linear regression and multiple linear regression? could u pls give some examples? do reply plzzz.. know my questions are idiotic, but i am really now out of help now.. u'll never imagine how excited i m now running into your videos. really life saver..(thumb up)
Good video, clear & thorough explanations!
Loved how you randomly explained how to check if .00 is smaller than .05 hahahaa :D
This is very clear! This really helped me out!
Thanks a lot sir!
I need this too Phil, much love you genius :) :) xx
I really needed this. Thank you so much Phil :)
Excellent! Thank you.
thanks. good explanation and useful for me.
Thank you very much!! It´s been incredibly helpful
Great video! Helped me in my assignment! Many thanks! :)
Thank you really its very helpful video,
Thanks so much. Keep it up! Really appreciate it
Thanks Phil! Big help!
You are literally a god, thank you so much!!!
It was good. I would like to know if the R found in the example is enough to consider the model as strong, and what are de intervale to decide about that.
The only thing I might add is some more info on the importance of each individual t stat inside the model as a whole
Very helpful thanks so much!
Beautiful!! big help!
Top man, top vid. Thank you
definitely helped me as well. thanks!
I just had question on how to write the Null Hypothesis, is it βs = 0 or βs > .05. Thank you for posting this video, it was very helpful.
The null is beta = 0 vs alternative hypothesis that beta does not equal 0.
Very helpful... Thank you!
THANK YOU! See you in the class!
Thank you!
Great job sir, thank you! :)
Thanks for the video. I would like to ask where can I find the Std. Error for the insignificant IVs?
Nice
Thank you that was incredibly helpful.
I'm working on my FYP,
thank you very much !!!!!!
Will it be too much to say I love you ? XD
if the t has negative value but it is significance which is p
this helped me a lot. Thank you.
Great vid. Can I ask what if the t-test is not significant? Should that particular IV be removed then redo the regression?
Hi. If you are a newbie/taking an intro course in modelling, then you are likely to be taught that since the coeff is not significant, you may try removing it an refitting. In reality, it's not so straight forward as the choice depends on numerous things like: reason for building a model, results of other (diagnostic) tests. But you can ignore my last comment if you are a newbie!
Great video!
thank you for great video. Could you use this method to select covariates to be entered into Ancova?
Hi Phil, thank you so much for this video, it really helps a lot. I just have one question, at the end of the video you say that the F and T statistics are valid if certain conditions hold for the model. Can you please list these conditions or point me towards material teaching about these conditions. Or maybe you have another video where you talk about this conditions. Thanks
Hello nice video but i have a question. How can i change the nominal or ordinal to scale??
Can I use the model if Goodness of fit provide Pearson Chi-Squire .000 but Deviance 1. However, model fitting information is significant .000
thank you very much!
very helpful indeed!
i have one question...how will you explain a negative Standard Coefficient Beta Value?
thanx
+Leo Christian Navarra Leo - the standardized coeff allows you to assess the relative importance of each IV to the others. To rank the importance of the Xs, drop any minus symbol in front of the coeff then compare the numbers.
+Phil Chan (statisticsmentorcom) thanx...God bless you and more power
Phil Chan, i don't know if you are still using this youtube account but if anyone can help me:I have found a significant value in the ANOVA table (0.024) but not any significant values in the coefficients table (0.054 and 0.132) what does this say?
+Phil Chan what does it mean if the constant in the coefficient table is non significant, but everything else in that table is significant? How do I interpret that?
+Klaudia C Klaudia - simple answer is to keep it in the model and ignore it. After all, the purpose of a regression model is for prediction (of the dependent variable) or interpretation of the coefficients on the predictors (Xs). For both, we don't have to be concerned with the intercept (the "constant" in SPSS).
+Phil Chan Thanks for the help!
Hi Phil,
Thank you for this video! It is very helpful.
What does the (Constant) represent? I am currently running a linear regression model in which other independent variables (including the total model) is significant, however the (constant) is not.
The "constant" is the intercept. I hope I mentioned in the video not to bother to see whether or not it's significant.
I have a question. What I should do if I would like to adjust model for potential confounding factors (covariates)?
Confounds are added to the regression, and so you'll be running a multiple regression.
You are great !!!
thanks
what to do when regression model is not significant, but some beta coefficents are significant
+bucko buckic A situation where the F-test for H0: model has no explanation power is not rejected, while some coeff in the model are individually sig is a contradiction, and so why could this be the case, and does it matter are 2 natural questions. 1) F and t tests are based on the standard assumptions of regression, which if not satisfied puts doubt in the outcome of the tests. Whether or not it even matters depends on whether you are building a model for prediction, or causal type model. For prediction one is not interested in interpreting the coeffs, and a different approach and focus on model building to one for causal type model.
is negative constant indicate any problem or abnormality?
Sir thank you! I have a question...if the whole model is rejected (ANOVA) considering the P-value then subsequent coefficients are also useless to be explained. My understanding we will explain and discuss about coefficients only when model is found to be fit with explanatory power... Kindly discuss it
The null hypothesis in F-test in the Anova table is that the model has no explanatory power for Y. If you reject this, and generally one does reject it, then you may have a look at the coefficients. Note, one should test the assumptions of regression (NICE-L assumption) before looking at the F and t tests.
Thank you, so can there be a case where overall model is rejected but coefficients are found to be having sufficient explanatory power
By the way you phrase your question, I wonder whether you understand the F-test. When you say the overall model is rejected, I take this to mean you reject to null of this F-test, and this means there is evidence the model has explanatory power. BUT perhaps you mean to say that suppose we DO NOT reject the F-test ie no evidence the model has explanatory power, then do the coeff have meaning. Supposing all the assumptions underpinning the model are satisfied then if there is no evidence the model has explanatory power those coeff don't tell us anything. Good luck.
Hi Phil
Cloud please write the predicting equations? I need much more. Thank you for this nice work. :)
Thanks man. May I know what the t value is for?
i could have known it like yesterday
What if the B is negative ? How do we interpret ?
Good video btw !
a negative coefficient would mean a fall rather than an increase, on average, in the value of the dependent variable with an increase in the predictor
Hello, This video is helpful, one more question: What is the meaning of the p value of the constant (0.029) in the last table? What if its > 0.05? it means the model is not significant or what? because I have two IVs, one is significant (.001) and another is not significant (.689) and the p value of the top column is .332, what is it stand for? Thanks
That's for the intercept. p-value for intercept >0.05 suggests it's not significant, but do not delete it. In applications, the importance on the slope not the intercept.
Thanks!!!
if my independent variable is 0.132 and the dependent variable is 2.554. how to write this
thanks, really helpful!
Thanks for the video and guidance!
But I have a question: I did a multiple regression analysis and the results showed that there were significant correlation between the DV and IVs, however in the coefficient table certain IVs had p-value higher than 0.05. How do I interpret this?
If I understand correctly, you looked at correlations between DV and IVs and found that some were significant, but then when you put them in the regression the coeff was insignificant. This is common place when running multiple regression. From a newbie perspective this could be because of multicollinearity problem (I have a video explaining this). Other reasons that could explain what you see in your analysis are mediation, and confounding variables. These last 2 need another video, which I have in the pipeline.
Phil Chan okay... will watch that video and see how things go. thank you!
Thank you so much for this! If my coefficient is very small (-0.004), how can I interpret that or how should I move the decimal? I hope this makes sense...
Supposing you are talking about a slope parameter
Think about the units of measurement your x that has the small param. A rescaling of the units of X would change the param. eg if X were measured in cents then changing units to 1000 dollars would change it to -4 from -0.004.
Yes thank you, I ended up figuring it out by rewatching the video! :)
Grazie