I am analyzing satisfactions level of Phd Scholar in a particular region, We got 7 Independent Variables from literature review and constructed 45 questionnaires accordingly , for example we 5 questionnaires related to stress, 4 questionnaires on scholar-guide relation etc ,

I have done reliability test of all the 45 items (combining all the items of 7 independent variable), we found Cronbach alpha 0.750 which is reliable but when i try to do reliability test for questionnaires related to only stress(only one independent variable) it is showing 0.44, similar for another independent variable showing below 0.7. Is it reliability test reliability necessary here as i have pick all the independent variable from Literature reviews but construct my questionnaires related it as it was not found in the LR
I have done step wise regression on the Mean(calculated compute variable) of 7 Independent variable and found this enter image description here

Model R R Square Adjusted R Square

1 .544a .295 .291
2 .584b .341 .334
3 .608c .369 .358

All the model is significant

I am evaluating two model using a testing set. The models are tailored to return a prediction for each instance, only if there is enough evidence that it is highly accurate. On the contrary, if there is no evidence for a test instance, then the models will not return a prediction for such instance.

This means that both models will return output vectors of different sizes. For example:

• Model 1 returns a prediction for 20% of the test set.
• Model 2 returns a prediction for 60% of the test set.

How can I perform a T-Test to compare the means of both approaches?

One of the solutions I have been thinking about is to compute the t-test only for the instances that both models managed to predict (Overlap).

Any suggestion?

P.D: Another solution would be to return a random prediction when there is not enough evidence, but I find this a bit misleading due to the nature of the task (predict a geolocation).

I am a graduate student in statistics, writing the methods and data analysis section of an epidemiology paper. I used an obscure but highly relevant statistical method to analyze our data. I genuinely believe this is the first novel application of this method outside of toy problems in highly theoretical paper.

What I want to say is: Previous research on METHOD XX has been limited to statistics and econometrics literature

This seems too sweeping a claim for me to make though. It’s entirely possible that someone, somewhere has used this method before. It is also a nightmare to cite. How do you cite the absence of evidence?

So, what is the best way to convey this? Is it as simple as tempering my initial statement a bit: Previous research on METHOD XX has largely been limited to statistics and econometrics literature. Should I get rid of this statement all together?

I would like to entroll in a masters program to learn Data Science.
I need to choose between two Universities. The University of Antwerp and Ghent University. Could you please provide me some advice or information which of these two higher institutions is better and why. Where I, as a student, have more opportunities to practice and improve my knowledge and skills in this domain.

I currently work as a software developer in a big IT company in my country. I am really passionate about data science and would like to be a professional data scientist in the future. But I don’t know which path to realize this aim is better. I have an opportunity to get a master’s degree in data science and engineering at universities abroad. On the other hand, I could stay in my current position and country and study the subject myself.

I don’t know which option to choose. Should I choose to enroll in a degree program, or study the subject on my own?

I would like to ask a little bit broad but very very important question.

I currently work as .NET software developer in one big IT company in my Country. I really passion for data science. I would like to be a professional data scientist in future. But I don’t know which path to realizing this aim is better.I have an opportunity to get a Masters degree in University of Antwerp(Belgium) or in Ghent University(Belgium) in specialisation DataScience and Engineering. From the other hand, I could stay in my current position and try to obtain necessary knowledge by myself.

I don’t know which option to choose. Should I choose MD in thees Universities or not?
If I should, which of this place is better for studying DataScience and why? If you know something about this higher institutions please provide me some information.

Hope for your support and understanding.

Thank you so much for your time!

I would like to ask a little bit broad but very very important question.

I currently work as .NET software developer in one big IT company in my Country. I really passion for data science. I would like to be a professional data scientist in future. But I don’t know which path to realizing this aim is better.I have an opportunity to get a Masters degree in University of Antwerp(Belgium) or in Ghent University(Belgium) in specialisation DataScience and Engineering. From the other hand, I could stay in my current position and try to obtain necessary knowledge by myself.

I don’t know which option to choose. Should I choose MD in this Universities or not?
If I should, which of this place is better for studying DataScience and why? If you know something about this higher institutions please provide me some information.

Hope for your support and understanding.

Thank you so much for your time!

The question title says it all.

I’m a college graduate in mathematics with a 3.73 GPA but have some poor grades in advanced specialty courses (major courses) in Math during my final year and a half in university.

Thus, I need to find a paying full time job for a few years and I need to get research experience because I currently don’t have anything noteworthy. I also don’t have any strong letters of recommendation.

However, needing to find a well paying job and doing my first research experience seem very contradictory. I don’t think I can do both simultaneously because both are day jobs.

How can I resolve this conundrum so I can go to a good graduate school in Statistics / Machine Learning? It is simply going to take five to seven years to work an industry job then find paid work in research?

I’m a math major, and in an undergraduate real analysis sequence I got a B- and B over the two semesters. A year later I took the first year graduate real analysis sequence and got a B+ and a B over the two semesters. I know there’s a lot of outside factors to consider, but in general how do these grades affect my ability to get a Master’s in Statistics and a PhD in Computer Science?

My overall GPA is an A-. How can I remedy this track record if it looks negative? Does it involve taking even higher level courses in Real Analysis (i.e., comparable to 6000 level) and getting A’s?