Data analytics, statistics, and more
Regression performed using censored data can be challenging. Common practices for handling censored data include deletion of the censored observations or substituting nondetects with arbitrary constants, generally based on some fraction of the detection limit. These approaches tend to be biased and cause a loss of information. Censored regression methods produce more accurate and robust estimates than these bias-prone methods.
Ordinary least squares regression is optimal when all regression assumptions are valid. When some of these assumptions are invalid, least squares regression can perform poorly. Robust regression is an alternative to least squares regression when data contain outliers or influential observations.
On January 28, 1986 the space shuttle Challenger disintegrated 73 seconds after liftoff from Kennedy Space Center. The most disturbing part of the space shuttle Challenger disaster was that the O-ring failure had been foreseen by the manufacturer’s engineers, who were unable to convince managers to delay the launch. Providing a better analysis and visualization of the data could have helped improve the decision-making process and potentially built a stronger case for the engineers about the effect of cold weather on O-ring functionality.
Power-law relationships are one of the most common patterns in the environmental. This post presents an example of the problems that can occur when fitting a nonlinear model by transforming to linearity using natural logarithms.
Dixon’s test is simple, easy to understand, and is widely used in the scientific community. Data recorded to some specific measurement increment can become a problem for outlier tests, such as Dixon’s test. Dixon’s test assumes that the data values (aside from those being tested as potential outliers) are normally distributed. Most sample distributions are not normally distributed.