China
Africa
Explore chapters and articles related to this topic
Descriptive Statistics
Published in William M. Mendenhall, Terry L. Sincich, Statistics for Engineering and the Sciences, 2016
William M. Mendenhall, Terry L. Sincich
Active nuclear power plants. The U.S. Energy Information Administration monitors all nuclear power plants operating in the United States. The table lists the number of active nuclear power plants operating in each of a sample of 20 states. Find the mean, median, and mode of this data set.Eliminate the largest value from the data set and repeat part a. What effect does dropping this measurement have on the measures of central tendency found in part a?Arrange the 20 values in the table from lowest to highest. Next, eliminate the lowest two values and the highest two values from the data set and find the mean of the remaining data values. The result is called a 10% trimmed mean, since it is calculated after removing the highest 10% and the lowest 10% of the data values. What advantages does a trimmed mean have over the regular arithmetic mean?
1001 Solutions
Published in Jaakko Astola, Pauli Kuosmanen, Fundamentals of Nonlinear Digital Filtering, 2020
Jaakko Astola, Pauli Kuosmanen
In Chapter 1 we recognized that both the median filter and the mean filter had desirable as well as undesirable properties. Most notably, the median filter discarded impulses well, but the case of additive Gaussian noise was more problematic for it. In turn, the performance of the mean filter was superior to that of the median in removing additive Gaussian noise but deteriorated dramatically with impulsive type noise. Therefore, good compromises between the median and the mean might lead to filters with good behavior in situations where both Gaussian and impulsive noise are present. Perhaps the simplest way to obtain this kind of compromise is to use trimmed means. Trimmed means probably date back to the prehistory of statistics since the idea of trimming out some suspicious looking samples is very obvious. Possibly the first article about trimmed means was published in 1821 [3]. The author is not known; he might have been Gergonne. The idea behind a trimmed mean is to reject the most probable outliers—some of the very smallest and very largest values, and after rejection to average the rest. Here we refer to Mendeleev (1895) [76]; (see [34]): I use… [the following] method to evaluate …: I divide all the numbers into three, if possible equal, groups (if the number of observations is not divisible by three, the greatest number is left in the middle group): those of greatest magnitude, those of medium magnitude, and those of smallest magnitude: the mean of the middle group is considered the most probable …
Fair compensation of crowdsourcing work: the problem of flat rates
Published in Behaviour & Information Technology, 2022
Joni Salminen, Ahmed Mohamed Sayed Kamel, Soon-Gyo Jung, Mekhail Mustak, Bernard J. Jansen
Times were calculated in minutes. The mean ± SD was used to summarise the distribution of the time taken to complete the surveys. The trimmed mean and coefficient of variation (CV) were also calculated for each study. The trimmed mean was calculated after excluding the top and bottom 10% of the data. The CV is a statistical measure of the relative dispersion of data points in a data series around the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. Conventionally, CV < 10 is considered excellent, 10–20 is considered good, 20–30 is acceptable, and >30 is not acceptable. Based on their worker IDs in the crowdsourcing platforms, the participants were prevented from taking part in more than one of the studies.
Assessing the impact of performance determinants in complex MTO/ETO supply chains through an extended hybrid modelling approach
Published in International Journal of Production Research, 2019
Cátia Barbosa, Américo Azevedo
The distribution of the manufacturing and assembly time, measured in hours, in Figure 6(b), shows three peaks, associated to the different assembly times of the MTO products, and presented in Table A1 of the appendices. The workload shows no correlation with the manufacturing and assembly time. Indeed, regardless the number of components that must be altered in each MTO project, the same number of components is produced and assembled for the same product. A histogram for the manufacturing and assembly time of the ETO projects, measured in hours, is presented in Figure 7(b). The histogram shows slightly positive skewed data (skewness value of 0.36), with a mean of 293.3 h, a standard error of the mean of 2.26 h, and a standard deviation of 100.97 h. The trimmed mean and the median values do not vary much from the mean, despite the slight data skewness. The MTO assembly and manufacturing times show significantly lower values when compared to ETO projects.