Slide 1
... • HF interferes with nerve function and burns may not initially be painful. Accidental exposures can go unnoticed, delaying treatment and increasing the extent and seriousness of the injury. • HF penetrates tissue quickly and is known to etch bone • HF can be absorbed into blood through skin and rea ...
... • HF interferes with nerve function and burns may not initially be painful. Accidental exposures can go unnoticed, delaying treatment and increasing the extent and seriousness of the injury. • HF penetrates tissue quickly and is known to etch bone • HF can be absorbed into blood through skin and rea ...
Solution to MAS Applied exam May 2015
... b. Observations from each treatment have a long-run average that depends on the treatment, but the a common variance between all treatments. All observations are mutually independent and ideally Normally distributed. Based on these boxplots, the assumption of equal variances seems shaky (seems like ...
... b. Observations from each treatment have a long-run average that depends on the treatment, but the a common variance between all treatments. All observations are mutually independent and ideally Normally distributed. Based on these boxplots, the assumption of equal variances seems shaky (seems like ...
2) Center or middle of the data values
... II) CATEGORICAL DATA – BINARY DATA In general, the summary statistics for categorical data are the relative frequencies of each category in the dataset. There is no such idea as a mean or average category, only the most common one or the least common one or some other appellation. For the special ca ...
... II) CATEGORICAL DATA – BINARY DATA In general, the summary statistics for categorical data are the relative frequencies of each category in the dataset. There is no such idea as a mean or average category, only the most common one or the least common one or some other appellation. For the special ca ...
Bootstrapping (statistics)
In statistics, bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed dataset).It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors.