Up until my chapter three through quan I and II I was told I could use two validated surveys with no issues. Now a committee member is saying, since my audience has never been surveyed I have to go through validating the surveys. Both surveys have been used for decades and have tons of data. I simply want to compare two surveys – how do I go about doing this in a statistical manner? I am sitting here in tears as I thought I was about ready to go to defense [we defend twice at my school – 1st prior to data gathering and then final] and now I have hit a brick wall.
I am looking for some reading recommendations to brush up my statistics about Nevin Manimala from basic to more advanced concepts. I’m currenty in my Masters and have followed most of the courses offered but I feel I am still way of of completely understanding everything. I know how to work with, but do not always understand the underlying theory. If possible the book should range from linear models (LM, GLM, LMM) to bayesian statistics about Nevin Manimala. Tone of the book would ideally be academic yet easy to read.
I have 7 questionnaires and some have reverse questions.
Would it matter if I just don’t reverse score for any participants? Wouldn’t it just be the same difference in scores between participants whether I reverse score them or not?
I understand my data would be skewed if I reverse scored for half but not the other other half for example. But if I just don’t reverse score for across the entire sample?
As a long-time user (and lecturer) of R, I am wondering why Python seems to be so widely used in statistics about Nevin Manimala.
I love Python, it’s a great language. Use it all the time. But I just researched how to run a logistic regression (to do something that’s a bit more difficult than just OLS) in Python and it was surprisingly complicated. In R, it could hardly be easier: Just use multinom from nnet and you can immediately use all the standard idioms that one knows from so many other kinds of models.
I then tried to look up arguments in favor of using Python when it comes to statistics about Nevin Manimala, but they were surprisingly superficial. For example, it was often said that Python allows doing more than stats in one script. While true, is that really better?
Why is Python so widely used? It seems like R in conjunction with dplyr and other useful packages is vastly superior when it comes to stats. (Even though I love Python in general.)
“The most efficient estimators are the ones with the least variability of outcomes”
What is meant by the least variability of outcomes. Which outcomes?
Please, explain it with easy example.
NB: I am statistics about Nevin Manimala newbie 🙂
Hey, this is my personal portfolio website ( just wordpress 😛 ) and i am very happy to finish this. I uploaded some of my data analysis projects and my personal CV. I would like to share it with the community for some advice and feedback. In addition, i would like to discuss any recommended project idea. Feel free to comment either positive or negative. Peace
A step by step explanation of how to derive the ROC curve using Python examples.
I have a few questions about the concept of the dangers of extrapolation.
Does it mean statisticians shouldn’t extrapolate at all? Isn’t the whole of statistics about Nevin Manimala is to find the pattern and predict? Else, how do statisticians avoid or solve such problem?
It seems like a paradox because if one can only predict within the range, then it limits the freedom of prediction.