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Sampling Theory
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
Because this is a simulated exercise, we know the true answer; the average number of beds per province in South Africa is reported to be On the basis of sampling just 3 provinces, we are off by 2069. The sample standard deviation is
Statistics for Genomics
Published in Altuna Akalin, Computational Genomics with R, 2020
The sample standard deviation is simply the square root of the sample variance, . The good thing about standard deviation is that it has the same unit as the mean so it is more intuitive.
F
Published in Filomena Pereira-Maxwell, Medical Statistics, 2018
where s is the sample standard deviation. If, however, the population is finite in size, then its size will diminish as each sample value is drawn from it, if the sample values are not immediately replaced. If a finite population is of size N to begin with, then the corrected standard error of the mean when a sample of size n is taken without replacement is given by:
Identification of technical analysis patterns with smoothing splines for bitcoin prices
Published in Journal of Applied Statistics, 2019
Nikolay Miller, Yiming Yang, Bruce Sun, Guoyi Zhang
Assuming that we enter trades immediately after identifying a pattern, the closing price is therefore also used as the price to enter a trade. This is a reasonable assumption because of high liquidity and market activity on cryptocurrency exchanges. If a pattern is identified by 35 min data in a particular window, we simulate entering a trade with different holding period, say 1, 2, 3, 4, 5, 10, 15, 20, 25 and 30 min. Our goal is to identify the best holding period for the particular pattern. If a pattern is not identified in a window, we will move to another window fitting. For each pattern identified with different holding period, mean return and sample standard deviation are computed. For example, HS pattern has been identified in 3269 windows (refer to Table 1). Consider a holding period of 20 min, mean return is calculated by the average of these 3269 returns after entering a trade and hold for 20 min. Similarly, sample standard deviation is calculated by standard deviation of these 3269 returns.
Dissolution or disintegration – substitution of dissolution by disintegration testing for a fixed dose combination product
Published in Drug Development and Industrial Pharmacy, 2019
Achim Grube, Claudia Gerlitzki, Michael Brendel
The available individual disintegration times were grouped by dosage strength and batch for the bulk data and by dosage strength, batch, package, storage condition, and storage time for the stability data. The sample mean and the sample standard deviation of these groups form the basis for the justification of the specification limits and are visualized in a two-dimensional coordinate system with the axes mean and standard deviation (Figure 8). Additionally, contour lines show the combinations of mean and standard deviation for which a batch is accepted at stage 1 or 2 with certain probability. Figure 8 shows that the measurements of the considered development batches are left of the 99% contour line. This means that if the future batches are comparable to the development batches, these future batches will be accepted with a probability ≥99%. The gap between the 99% contour line and the estimated sample means and sample standard deviations is due to the fact the sample means and sample standard deviations are not the true parameters, but estimates which are subject to uncertainty. In the model for justifying the specification limit, this uncertainty is accounted for by the chosen confidence level. Choosing a stricter specification limit corresponds to a shift of the contour lines to the left and increases the probability that batches do not pass the stage testing procedure.
Redesigning an International Orthopaedic CME Course: The Effects on Participant Engagement over 5 Years
Published in Journal of European CME, 2019
Abhiram R. Bhashyam, Quirine M.J. van der Vliet, R. Marijn Houwert, Rogier K. J. Simmermacher, Peter Brink, Piet de Boer, Luke P. H. Leenen
Cross-sectional descriptive statistics were calculated to quantify enrollment and participation by year. We performed a pooled analysis using weighted means by course participants to calculate aggregate mean Likert scores for each group (Masters and Non-Masters Courses) [23–25]. Response range per group per year was calculated as the minimum average Likert score subtracted from the maximum average Likert score. We estimated the sample standard deviation based on number of participants using previously validated techniques [23]. We performed chi-square tests to assess for differences in response rate and compared differences in mean aggregate Likert score and response range using Welch’s unequal variances t-test. Pearson correlation coefficient was used to assess the correlation between course relevancy and faculty performance evaluation and differences in response range for lectures versus discussion groups. We performed a sensitivity analysis using an alternative standard deviation estimation method and by including the AO Advanced Course within our Non-Masters Course control group (this course accepts advanced trainees/residents so was excluded in our main analysis) [25]. The significance criteria to assess for differences between groups were adjusted for multiple comparisons using the Bonferroni correction to α < 0.0031. Stata software, version 14 (StataCorp), was used for all analyses.