Contents
When should you not weight?
A general rule of thumb is never to weight a respondent less than . 5 (a 50% weighting) nor more than 2.0 (a 200% weighting). Keep in mind that up-weighting data (weight › 1.0) is typically more dangerous than down-weighting data (weight ‹ 1.0).
What is a weighted variable?
A weight variable provides a value (the weight) for each observation in a data set. Observations that have relatively large weights have more influence in the analysis than observations that have smaller weights. An unweighted analysis is the same as a weighted analysis in which all weights are 1.
Why do I weigh more after I poop?
If you were to weigh yourself before and after pooping, the weight change on the scale would reflect the weight of the stool, which also contains protein, undigested fat, bacteria, and undigested food residues. Of course (and unfortunately), this doesn’t mean you’ve lost weight.
Why do I weigh less after a shower?
Actually, the answer to your question is simple…the warmer/ hot water in the shower raises your body temperature. This helps ” melts ” away a small amount of your fat cells closest to the surface of your skin, causing you to sweat both it and water out…and in doing so, weight.
When to use O ( 1 ) in weighted random choice?
O (1) in all cases. If the weights are successive powers of 2 (e.g. [32,16,8,4,2,1]), then if the algorithm generates a very high random number, it might traverse the whole list of weights, one element at a time.
How to choose a random number with weight?
I’m trying to devise a (good) way to choose a random number from a range of possible numbers where each number in the range is given a weight. To put it simply: given the range of numbers (0,1,2) choose a number where 0 has an 80% probability of being selected, 1 has a 10% chance and 2 has a 10% chance.
How to make a weighted random selection algorithm?
This can be achieved by dividing the weight of a selection by the total weight of all the selections, thereby normalizing them to 1. then a random selection is made similar to how the roulette wheel is rotated. Typical algorithms have O (N) or O (log N) complexity but you can also do O (1) (e.g. Roulette-wheel selection via stochastic acceptance ).
How to get weighted random items in software?
This approach requires more complicated code than the first, but less memory and computation and supports floating-point weights. For either algorithm, the setup-step can be done once for an arbitrary number of random selections.