Hi, the log odd of 0.111 shows -2.29 while I'm using function log(0.1111) in R. As per your tutorial the values is -0.95424. May I know the result is different to me.
can we have the same thing for Rank variable? like what happens to the chance of getting admitted if the Rank variable is 1,2,3 or 4. How do we interpret for sets of equations then?
ARIMITRA MAITI Thats an interesting question. Well, one of the rank is taken as base rank as the other ranks are compared to the base rank...Like for example for two students having exactly same GRE and GPA score, what is difference in the odds of getting admitted if one is from rank1 college compared with the one from rank4, similarly rank2 compared to rank 4, and rank 3 with rank 4 (rank4 being the base rank)
He chose 300 as a random example of GRE score and compared it to 301. Results would have been the same had he randomly chosen to compare 200 to 201, or 150 to 151, etc.
Learn Credit Risk Analytics (POP, PD, LGD, EAD, CCF, Stress Testing, Model Validation ) : analyticuniversity.com/credit-risk-analytics-study-pack/ . Contact : analyticsuniversity@gmail.com
Complete Data Science Course : bit.ly/34Sucmb
Data Science Books on Amazon :
Python Data Science : amzn.to/2Qg6g8m
Business ANalytics : amzn.to/2F7RhGT
STatistics : amzn.to/2ZGcSjb
Statistical Leanring : amzn.to/2ZHV6fn
Python : amzn.to/2u0uKJR
Audio books : amzn.to/2SSynMD
Coursera :
Data Science : bit.ly/37nABr6
Data Science Python : bit.ly/2ZK5oMm
Discounted courses on Udemy: bit.ly/2LYU6hp
Udacity Nanodegree:
Data Science : bit.ly/39IzAfc
Machine Learning : bit.ly/2sOinRb
Free access to Skillshare: bit.ly/2thklJu
20$ discounts on below courses : use coupon UA-cam20
Data Science Live Training :
AI and Tensorflow: bit.ly/2tOnOzA
Python : bit.ly/2QkH1QQ
Data Analytics : bit.ly/2PR4eez
Data Science : bit.ly/2QhxmdR
SAS : bit.ly/2Mpx83m
Big Data Training:
Hadoop : bit.ly/2sgHWdb
Splunk : bit.ly/2Ms0A8L
Kafka : bit.ly/2MorRc4
SPark : bit.ly/35TO8Gp
super informative I finally understand log odds!!
Unless I am mistaken, it would seem to me that at 9:28, Interpreting Log Odds, the formula should be Exp (Logodd (301) - Logodd (300)), not 300-300.
In an even or odd game, what algorithm should we apply, sir?
Thanks!
Hi, the log odd of 0.111 shows -2.29 while I'm using function log(0.1111) in R.
As per your tutorial the values is -0.95424. May I know the result is different to me.
also is there any video for say ROC curves and lift curves interpretation?
very good interpretations. very helpful.
NIce video sir..!!
Awesome
It was helpful for me
can we have the same thing for Rank variable? like what happens to the chance of getting admitted if the Rank variable is 1,2,3 or 4. How do we interpret for sets of equations then?
ARIMITRA MAITI Thats an interesting question. Well, one of the rank is taken as base rank as the other ranks are compared to the base rank...Like for example for two students having exactly same GRE and GPA score, what is difference in the odds of getting admitted if one is from rank1 college compared with the one from rank4, similarly rank2 compared to rank 4, and rank 3 with rank 4 (rank4 being the base rank)
ARIMITRA MAITI
Thank You!!
thank you!
@7.14 can anyone tell how he assume value 300??...................then he got the difference of log odds .00358?
He chose 300 as a random example of GRE score and compared it to 301. Results would have been the same had he randomly chosen to compare 200 to 201, or 150 to 151, etc.
Hello, what if the odds ratio is below 1, lets say 0.89***?
Can you do similar video for marginal effect?
It's already done
ua-cam.com/video/GgFLY6J07so/v-deo.html