Комментарии:
what if the null hyphosis is true, no significant correlation between these variables?
ОтветитьHi, Can you run a regression when the y variable is a range and not a single number. For example range of income is the x variable and the years worked is the y variable
ОтветитьThank you for sharing such a detailed and high quality video.
ОтветитьThanks a lot for such clear explaination
ОтветитьI loved the fact that, the content is so simplified and easy to digest
ОтветитьThanks for this. This is a super big help.
ОтветитьThanks a lot 😊
ОтветитьCan you please do a tutorial on multiple regression?
ОтветитьCan you please do a tutorial on multiple regression
ОтветитьHow to interpret the accuracy of the linear Regression Model?
ОтветитьYour teaching method, language, and presentation are just fantastic!
ОтветитьMan your explanation is very clear. Thank you 👌👌
ОтветитьVery rare to find a video that is detailed and easy to understand like this one... superb indeed.
ОтветитьHi Steven! Why no one teach how to do the graphs as well? You used the tools but if you don't show how there is no difference between you and wikipedia. It can be seen that you put a lot of effort in the video but look back to the information delivered, there is no value! Take care and hope you do more next time!
ОтветитьThis guy has help me a lot after struggling on how to do this analysis on data.
ОтветитьCan you put this data in description
ОтветитьCan you drop this excel data
ОтветитьPerfect explanation
ОтветитьWOW that video is super helpful, thank you so much for posting it.
Ответитьthis is very helpful thank you
ОтветитьThank you so much
ОтветитьGreat explanation!
If I would like to practice following your video, where I can download the data?
Hello. Thank you for your video. I have a question. I followed the steps and put your exact data in an Excel spreadsheet and performed the regression analysis. My results were very slightly different from yours. For example, your Multiple R is 0.657445249 but mine is 0.657525711. And a similar slight difference happned in the other variables, too. Can you please explain the reason? Thank you.
ОтветитьTop class video. Super like.
ОтветитьThank you so much for such a wonderful video.
May I have the excel file, so one can practice along with the tutorial.
Do you have a video on how to do this but with a multiple regressor?
ОтветитьThanks a tonn❤
ОтветитьHigh quality explanation
ОтветитьI just subscribed. Excellent video. Absolutely perfect analysis
ОтветитьVery helpful video thank you so much!
ОтветитьExcellent job! Congratulations!
ОтветитьExcellent! Best linear regression video I have seen. Thank you!
ОтветитьI freaking LOVE technology! this is amazing! The world is so lucky to have people like you!!! Thank you!
ОтветитьThank you so much for going through this in such detail. This was extremely helpful for me. I appreciate you!
ОтветитьFinally a video that show in deeper detail all the coefficients and charts generated by Excel in a Regression analysis. Thank you very much for that.
ОтветитьThanks a lot man!
ОтветитьWhat a legend
ОтветитьSuperb! This is excellent. Very much appreciate your in-depth coverage of each of the elements in the output
Ответитьsuperb little tutorial thanks.
ОтветитьHi
ОтветитьThank you for the most comprehensible piece, I am forever thankful!!!!
ОтветитьHat's off to u sir for this great video
ОтветитьThank you for sharing. Coming from a non statistical background, you really helped my understanding by the way you simplified the statistical analysis.
Ответитьthank you, a thousand times, thank you, Dr. B, from a non-traditional (i.e., OLD) doctoral candidate dissertating in acoustic and socio-linguistics. Is that a Mancunian accent I hear, Oldham, perhaps? Peace, kw
ОтветитьThank you! one of the best tutorials videos i have seen. Very clear and practical.
Ответитьplease share the excel file
Ответитьthank you my G
ОтветитьI ran a simple linear regression in Excel twice, each time I swapped which variable was X and which was Y. While obviously the units of the standard error changes, and the residuals changed, the correlation coefficient remained the same and the p-value remained the same. I thought linear regression established cause and effect, but it seems it only establishes a better prediction of what you can expect to happen to one variable if the other variable changes by some amount. Basically, is it still only a measure of correlation rather than cause and effect?
ОтветитьSo helpful. Thank you so much!!!
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