2019年7月8日 · Optimization on RMSE yields an average-based number... whereas on MAE yields a median-based forecast. Forecast KPI: RMSE, MAE, MAPE & Bias. Advantages of using median forecast: robust to outliers. Disadvantages of using median forecast: bad for intermittent time series data, medians can be biased for non-normal data, median forecasts are not ...

I am trying to summarize the data in a single value and I would normally expect to use the harmonic mean. However, a co-worker suggested that a median might be a better measure. I can't think of an argument for or against using the median to summarize the data ("average") versus using the harmonic mean. What are some things I should consider?

2013年10月22日 · $\begingroup$ My concern is that factual characteristics of the mean and median (e.g. the former is sensitive to outliers, viz "People of such age have 1.5 to 4 times the influence on the mean than they do on the median compared to very young people.") become translated into values about their worth, viz "the median gives us a slightly better picture of …

2022年11月20日 · $$\text{median}(X) = x:F(x) = 0.5$$ The median tells little about the entire distribution and what the tail of the distribution does. The median can be even zero if more than 50% of the deviation is zero. The mean on the other hand, gives a more weighted information about the entire distribution and includes the tails.

But the median (the value with half the observations greater and half less than it), the mode (the most common value), the geometric mean (multiply the values then take the nth root), the harmonic mean (the reciprocal of the mean of the reciprocals of the data), and others all fall under the general term "average."

The median is related to the mean in a non-trivial way but you can say a few things about their relation: when a distribution is symmetric, the mean and the median are the same . when a distribution is negatively skewed, the median is usually greater than the mean . when a distribution is positively skewed, the median is usually less than the mean

In the example you mentioned, the median (162) is larger than the mean (129), which means there are more observations towards larger values than towards smaller ones. In your context, this means that the lifespan of the subscribers in that industry tend to be larger, with a few low-valued observations pulling down the value of the mean.

2022年2月10日 · A boxplot will give you median and you can add a notch to show the 95 % CI for the median so it is quick and easy to compare different samples and see if the CI overlaps. Mean with 95% CI will give you almost the same, but the data must have a normal distribution and you don't get information about quartiles and outliers?

2015年3月27日 · If there is a dataset that have great outliers, I'll prefer median. E.x.: 99% of household income is below 100, and 1% is above 500. On the other hand, if we work with wear of clothes that customers give to dry-cleaner (assuming that dry-cleaners' operators fill this field intuitively), I'll fill missings with mean value of wear.

2021年7月30日 · There are multiple generalisations of the median to higher dimensions, all trying to preserve some properties of the median, and they are all different. A geometric median is like a medoid except it doesn't have to be one of the sample points; this paper reviews some other definitions of higher-dimensional medians