# Multinomial logistic regression using SPSS by G N Satish Kumar
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- Опубликовано: 21 авг 2024
- Multivariate Data Analysis : Multinomial Logistic Regression
Multinomial Logistic Regression is used to analyze when the dependent data is categorical and having more than 2 levels.
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Very clearly explained. Many Thanks Sir
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Professor, i am realllllly reallllly thankful to you for this explanation and saved my research desertions. i really want to kiss your hands in respect for this great lecture and explanation. deep from my heart, my respect and love for you
Thank you so much.
hi @My Easy Statistics, thanks for the helpful videos. i really apreciate. i want to ask you, how the marginal effect when using Multinomial logit, i mean how to get it. another question is there a difference betwee binary and Multinomial logit models and binary and Multinomial logistic regression analysis
thankyou sir you helped me in my thesis at last minute ... so grateful for your knowledge !
Thank you sir, it's clear, complete and easy to understand
Sir, this is a very informative video. Thanks for providing this
Video is very helpful and easy to understand. Thank you very much 👍
thank you for this best vidoes
Nice video sir🙏
Wonderful instruction of the subject.
Thank you
Thank you for this very easy to understand video:))
Thanks
Really helpful
thanks sir
very very thank you, really awesome. do you have any videos of Conditional logistics regression
Sir please show us the data arrangement for an ordinal and nominal variable in data view, it will be helpful for.
Thanks a lot for the very helpful video. But aren't we suppose to interpret the parameter estimates using the exponential (B) instead of only the coefficient (B)?
Yes. You are right
Thank You
Thank you!
Respected Sir, Thankyou for explaining the outputs of multinomial regression model in such an easy way. My Pranams to you.
Respected Sir, I have some doubts regarding how to write the values got in parametric expression.
I have included the values of B, Std. Error, Wald, DF, Sig. for those independent variables that are significant.
But, I see in most of the research papers, at the end of the table there is Constant written at the last,
where from are these values got.
constants
Thank you for the video. I note you were reporting the 'B' values instead of Exp(B). In binary logistic regression I've always taken the Exp(B) to be the actual odds ratios that I report in my papers along with the 95% confidence interval. Does this still remain true for multinomial regression?
ruclips.net/video/4bbns_WDxLA/видео.html
Sir, really the vedio is very informative . sir I have one doubt in the out put.... following is the warning message..... " There are 88679 (46.1%) cells (i.e., dependent variable levels by subpopulations) with zero frequencies".
sir pls clarify it
Thank you very much. Is it possible to also do this, if the independent variable has more than 2 outcomes?
If possible can you write the null hypothesis of Chi Square goodness of fit table by which we will be either accepting or rejecting the null hypothesis. Sir, can you also tell me the interpretation difference between categorical and continuous independent variables in multinomial logistic regression
Thank you sir for clear explanation.but i have one question i running multinomial logistic regression likelihood ratio(chi square) is significant but the parameter is insignificant ?
a significant likelihood ratio test indicates that the model as a whole fits the data well, but individual parameter estimates may still be statistically insignificant for various reasons. It's essential to interpret the results of multinomial logistic regression in the context of the research question, considering both the overall model fit and the significance of individual coefficients. Additionally, examining effect sizes and confidence intervals can provide valuable insights into the practical significance of the results.
Thankyou for the vedio sir..Please tell how we do it when we have more numbers of variables as In body mass index..Sir please do reply
Thankyou sir for your clear explanation. I have a doubt can I consider level of education as a dependent variable with respect to other independent variables like utility of a given product, age etc.,. And how many independent variables can be considered at a time in multinomial regression analysis.
Education level can not be a dependent variable and there is no limit for number on independent variables.
Hi, do you have any idea, what type of regression that suitable for the small number of data? the aim of the analysis is to see how is the effect of an independent variable to the dependent variable. Thank you
thank you very much :)
Please mention what are the assumptions and diagnostic tests for multinomial logistic regression
Yes please
Hi sir.I really, really need your help and advice. May i share my spss problem with you? i have this warnng message : "Unexpected singularities in the Hessian matrix are encountered. This indicates that either some predictor variables should be excluded or some categories should be merged.
The NOMREG procedure continues despite the above warning(s). Subsequent results shown are based on the last iteration. Validity of the model fit is uncertain'.
I am not sure how to troubleshoot this sir. I am a newbie. Google, youtube, researchgate and reddit could not help me =(
Sir I have few doubts. Please help me.
Doubt number 1: In my study, under "Goodness of fit" Pearson is significant and Deviance is non significant. In this case should I have to continue my analysis or not
Doubt number 2: after pseudo r square table, there is likelihood ratio test. In that likehood ratio test, few of my variables are significant and few are not significant.
So , for further analysis( parameter estimates) do I have to consider only variables which came significant in likelihood ratio test???
Please do help me
Thank you
thanks!
sir, I am running multinominal logistic regression and the likelihood ratio (chi square) is insignificant, but the predictor is significant in the parameter estimates. Should i have to interpret them or i have to avoid them in my analysis?
Sir, Please explain how to interpret Standard Error and Confidence Interval. Thanks
the Standard Error quantifies the precision of an estimate, while the Confidence Interval provides a range of plausible values for the true population parameter. Both SE and CI are crucial for understanding the reliability and uncertainty associated with statistical estimates derived from sample data.
@@MyEasyStatistics Sir,
Thanks 😊
can i make dummy by myself in spss
?
+SB RAFI yes
+SB RAFI yes
relative risk ratios
Sir what if someone needs this data set of your lectures
Please send your email I'd. I will forward the data set
@@MyEasyStatistics ambreenmeroline@gmail.com
Can we have this dataset?
great lecture...! i need to consult u for my research problem.....kindly share ur e-mail id sir...
+Shagafta Khan myeasystatistics@gmail.com
+Shagafta Khan myeasystatistics@gmail.com
gnsatishkumar@gmail.com , myeasystatistics@gmail.com
Can i have the data Sir?
can you mail me this data set?