Hey there Linda. I hope you're well. I am currently studying for my postgraduate qualification in data analytics and this video helped me out so much. Thank you for taking the time and effort to upload this and I hope you continue with these videos.
Great video....looking for this for years.....One Question: If you run SOLVER 2 times in a row on the same project....What does those "B" values turn into? Thx
Great question! (Sorry for the long answer, but it's an important component of this approach to regression). In logistic regression, the natural log function is used in the likelihood because of its properties that make it well-suited for modeling probabilities. The logit function, which is the natural log of the odds ratio, converts the probability of success to a linear function of the predictors. It also converts the probability of success to a linear function of the predictors. This linear relationship between the log odds and the predictors is convenient for modeling with linear regression techniques.
Hey there Linda. I hope you're well. I am currently studying for my postgraduate qualification in data analytics and this video helped me out so much. Thank you for taking the time and effort to upload this and I hope you continue with these videos.
I'm glad it helped. Using Excel for this is a little clunky, but it gets the job done! Good luck with your studies
fantastic video!! well explained and very concise. saved me a long night of struggling to learn R haha
That's the whole reason I made this video - finding a way to do this WITHOUT R! Glad this helped
Great video....looking for this for years.....One Question: If you run SOLVER 2 times in a row on the same project....What does those "B" values turn into? Thx
hey, thanks for effort you put into this,
i have a question, why did you use a natural log function in the likelihood!
Great question! (Sorry for the long answer, but it's an important component of this approach to regression).
In logistic regression, the natural log function is used in the likelihood because of its properties that make it well-suited for modeling probabilities. The logit function, which is the natural log of the odds ratio, converts the probability of success to a linear function of the predictors. It also converts the probability of success to a linear function of the predictors. This linear relationship between the log odds and the predictors is convenient for modeling with linear regression techniques.
Thank you.