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Diagram showing the cumulative distribution function for the normal distribution with mean (μ) 0 and variance (σ 2) 1. These numerical values "68%, 95%, 99.7%" come from the cumulative distribution function of the normal distribution. The prediction interval for any standard score z corresponds numerically to (1 − (1 − Φ μ,σ 2 (z)) · 2).
The commonly used approximate value of 1.96 is therefore accurate to better than one part in 50,000, which is more than adequate for applied work. Some people even use the value of 2 in the place of 1.96, reporting a 95.4% confidence interval as a 95% confidence interval.
What fraction of pay depends on performance, and what is meant by performance, can vary widely. [1]Research on extreme high-stakes incentives [2] funded by the Federal Reserve Bank undertaken at the Massachusetts Institute of Technology with input from professors from the University of Chicago and Carnegie Mellon University repeatedly demonstrated that as long as the tasks being undertaken are ...
Given a sample from a normal distribution, whose parameters are unknown, it is possible to give prediction intervals in the frequentist sense, i.e., an interval [a, b] based on statistics of the sample such that on repeated experiments, X n+1 falls in the interval the desired percentage of the time; one may call these "predictive confidence intervals".
Occupational inequality greatly affects the socioeconomic status of an individual which is linked with their access to resources like finding a job, buying a house, etc. [4] If an individual experiences occupational inequality, it may be more difficult for them to find a job, advance in their job, get a loan or buy a house.
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Another common class of metrics is to take the ratio of the income of two different groups, generally "higher over lower". This compares two parts of the income distribution, rather than the distribution as a whole; equality between these parts corresponds to 1:1, while the more unequal the parts, the greater the ratio. These statistics are ...
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