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Here's an example of an application in my work. I'm mainly going to be running mechanical tests to collect strain gage data to quantify the mechanical properties of composite materials. I want to be able to develop a statistical basis on the number of test samples, and given a large enough sample size, develop some standard mechanical properties.
Here's an example of some standard statistical methods we use in the aviation industry to accomplish this. I can obviously follow the equations, but I want to be able to understand how the equations were derived so I can explain them to my colleagues and outside parties (e.g. auditors). And if necessary develop my own statistical tests if the prescribed ones either don't work or aren't applicable.
Hey all, I'm hoping some of you can point me in the right direction. I'm looking for books that will give me a good mathematical foundation in statistics so that I can apply it to my engineering work, and Python.
I have a B.Sc in Aerospace Engineering, so I have some formal training in math but only up to the usual differential equations & linear algebra. I took one undergrad course in statistics a long time ago as part of my Applied Math minor, but the professor was pretty terrible and I didn't learn anything.
A few years ago I picked up Python (mainly as a hobby), but I've been finding more and more use for it in my professional work as an aerospace engineer. Specifically, the use of pandas
for data analysis, and utilizing Jupyter notebooks as a formal documentation process. I do machine learning stuff on the side as a hobby, and I also have a long-standing interest in econometrics.
After searching both r/statistics and /r/Python, here's my current reading list:
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2e) by Wes McKinney
Statistics (4e) by David Freedman
Introductory Statistics and Analytics: A Resampling Perspective by Peter C. Bruce (review here)
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