Robust regression | Wikipedia audio article
Channel: wikipedia tts
Date: 06/10/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Robust_regression


00:00:48 1 Applications
00:01:12 1.1 Heteroscedastic errors
00:02:01 1.2 Presence of outliers
00:02:50 2 History and unpopularity of robust regression
00:03:38 3 Methods for robust regression
00:04:03 3.1 Least squares alternatives
00:04:51 3.2 Parametric alternatives
00:06:28 3.3 Unit weights
00:07:17 4 Example: BUPA liver data
00:08:06 4.1 Outlier detection
00:08:54 5 See also
00:09:43 6 References



Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.

Learning by listening is a great way to:
- increases imagination and understanding
- improves your listening skills
- improves your own spoken accent
- learn while on the move
- reduce eye strain

Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.

Listen on Google Assistant through Extra Audio:
https://assistant.google.com/services/invoke/uid/0000001a130b3f91
Other Wikipedia audio articles at:
https://www.youtube.com/results?search_query=wikipedia+tts
Upload your own Wikipedia articles through:
https://github.com/nodef/wikipedia-tts
Speaking Rate: 0.9202726852282737
Voice name: en-AU-Wavenet-A


"I cannot teach anybody anything, I can only make them think."
- Socrates


SUMMARY
=======
In robust statistics, robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods. Regression analysis seeks to find the relationship between one or more independent variables and a dependent variable. Certain widely used methods of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results if those assumptions are not true; thus ordinary least squares is said to be not robust to violations of its assumptions. Robust regression methods are designed to be not overly affected by violations of assumptions by the underlying data-generating process.
In particular, least squares estimates for regression models are highly sensitive to outliers. While there is no precise definition of an outlier, outliers are observations which do not follow the pattern of the other observations. This is not normally a problem if the outlier is simply an extreme observation drawn from the tail of a normal distribution, but if the outlier results from non-normal measurement error or some other violation of standard ordinary least squares assumptions, then it compromises the validity of the regression results if a non-robust regression technique is used.