腾讯哈勃
Simple OLS Regression, Pairs Bootstrap Resampling, and Hypothesis Testing to observe the effect of Hubble’s Law in Python.
通过简单的OLS回归，配对Bootstrap重采样和假设检验来观察哈勃定律在Python中的效果。
In this post, we will revisit Hubble’s Law and examine the original dataset he used by running an Ordinary Least Squares Linear Regression on the 24 measurements of distances and recessional velocities of extra-galactic nebulae. Then, we will use a pairs bootstrap resampling to calculate the RSS Minima and perform a hypothesis test on the measured effect of galactic distance on recessional velocities.
在本文中，我们将回顾哈勃定律，并通过对银河外星云的距离和后退速度的24个测量值进行普通最小二乘线性回归来检验他使用的原始数据集。 然后，我们将使用成对的自举重采样来计算RSS最小值，并对银河距离对后退速度的测量影响进行假设检验。
Based on the results of the hypothesis test we can conclude with a high degree of statistical signficance that distance has an observed effect on the recessional velocity of galaxies. This is concrete evidence of Hubble’s Law that the universe is constantly expanding.
根据假设检验的结果，我们可以得出高度的统计意义，即距离对星系的后退速度有明显影响。 这是哈勃定律证明宇宙不断膨胀的具体证据。
Before we get into that let’s familiarize ourselves with Hubble’s Law.
在开始讨论之前，让我们熟悉哈勃定律。
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