Gaussian - Standard Normal Distribution

Gaussian - Standard Normal Distribution

免费 STL 数码配件
  • 13 次浏览

该模型托管于 Cults3D。文件、图片及许可信息归原创作者所有。

在 Cults3D 查看 ↗

简介

This Distribution was made using the profile of the Standard normal distribution revolved about the Z axis. It is an approximation, that I drew free hand (tracing a screen capture from https://www.desmos.com/calculator/2kmx0enkkz) to get a rough idea of how the percent per sigma differs from the percent per sigma by volume. I used Blender to measure the volumes (before making space between each cylinder so that they can nest). The running percent totals (by volume) are roughly as follows: 1 sigma - 36% 1.25 sigma ~ 50% 2 sigma - 81 3 sigma - 97% 4 sigma ~ 100% Individually they are: 1 sigma - 36% 1 sigma to 1.25 sigma - 14% 1.25 sigma to 2 sigma - 31% 2 sigma to 3 sigma - 16% 3 sigma to 4 sigma - 3% I made it so that you could arrange 50% of the total distribution by volume in two parts by nesting the first piece inside the second piece. Some things that I learned: -for a 2D Gaussian the 50%, by volume, mark is at .6745sigma, but for a 3D Gaussian it is closer to around 1.25sigma, which I did not expect. -like the previous, 1 sigma for a 2D contains 68% while for a 3D it only contains roughly 36% This wasn't meant to be an exact calculation, nor was it generated using a formula. It does make a decent approximation, and a nice demo piece. I think this helps to demonstrate that distributions are different when going from 2D to 3D. I would print it at a scale where the biggest ring is about 9cm OD.

About this model

Gaussian - Standard Normal Distribution is a 免费 3D model in the 数码配件 category, shared by the Cults3D community. You can download it for 3D printing and reuse it in your own projects under the terms of its original license.

File formats

  • STL — The most common mesh format for 3D printing — ready to slice and print on almost any printer.

The files for this model are hosted on Cults3D. Use the button above to open the original page and download it.

相似模型