You would need to take repeated samples, find their median and then calculate the standard error of these values.
The answer will depend on what you mean by "solve". Find the mean, median, mode, variance, standard error, standard deviation, quartiles, deciles, percentiles, cumulative distribution, goodness of fit to some distribution etc. The question needs to be a bit more specific than "solve".
1. A nonparametric statistic has no inference 2. A nonparametric statistic has no standard error 3. A nonparametric statistic is an element in a base population (universe of possibilities) where every possible event in the population is known and can be characterized * * * * * That is utter rubbish and a totally irresponsible answer. In parametric statistics, the variable of interest is distributed according to some distribution that is determined by a small number of parameters. In non-parametric statistics there is no underlying parametric distribution. With non-parametric data you can compare between two (or more) possible distributions (goodness-of-fit), test for correlation between variables. Some test, such as the Student's t, chi-square are applicable for parametric as well as non-parametric statistics. I have, therefore, no idea where the previous answerer got his/her information from!
Percent error refers to the percentage difference between a measured value and an accepted value. To calculate the percentage error for density of pennies, the formula is given as: percent error = [(measured value - accepted value) / accepted value] x 100.
Percentage error = Value experimental-Value acceptedValueaccepted x 100
The parameters of the underlying distribution, plus the standard error of observation.
You calculate the standard error using the data.
The answer will depend on what you mean by "solve". Find the mean, median, mode, variance, standard error, standard deviation, quartiles, deciles, percentiles, cumulative distribution, goodness of fit to some distribution etc. The question needs to be a bit more specific than "solve".
1. A nonparametric statistic has no inference 2. A nonparametric statistic has no standard error 3. A nonparametric statistic is an element in a base population (universe of possibilities) where every possible event in the population is known and can be characterized * * * * * That is utter rubbish and a totally irresponsible answer. In parametric statistics, the variable of interest is distributed according to some distribution that is determined by a small number of parameters. In non-parametric statistics there is no underlying parametric distribution. With non-parametric data you can compare between two (or more) possible distributions (goodness-of-fit), test for correlation between variables. Some test, such as the Student's t, chi-square are applicable for parametric as well as non-parametric statistics. I have, therefore, no idea where the previous answerer got his/her information from!
Mean: 26.33 Median: 29.5 Mode: 10, 35 Standard Deviation: 14.1515 Standard Error: 5.7773
you calculate the degree of accuracy and divide it by 2
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To calculate percent error with multiple trials, find the average of the trials, then calculate the percent difference between the average and the accepted value. Divide this difference by the accepted value and multiply by 100 to get the percent error.
The standard error of the underlying distribution, the method of selecting the sample from which the mean is derived, the size of the sample.
For a relative error maybe it is: (Vout_hi - Vout_lo) / (Vout_hi_nom - Vout_lo_nom) - 1
The truncation error is the difference between two sides of an equation. Each side has an error value which can be compared.
To calculate the error between two values, subtract the smaller value from the larger value and take the absolute value of the result.
The standard deviation associated with a statistic and its sampling distribution.