Lecture 3: Statistical sampling uncertainty
... to break the time series up into chunks which are long enough not to be strongly correlated with each other, perform the data analysis on each chunk separately, and compare the results for different chunks. In testing for the uncertainty of trends, it can also be useful to resample the data (known i ...
... to break the time series up into chunks which are long enough not to be strongly correlated with each other, perform the data analysis on each chunk separately, and compare the results for different chunks. In testing for the uncertainty of trends, it can also be useful to resample the data (known i ...
Actually, like the z-interval procedure, the t
... and the sample size is small or moderate, provided the variable is not too far from being normally distributed. In other words, the t-interval procedure is robust to moderate violations of the normality assumption. When considering the t-interval procedure, it is also important to watch for outliers ...
... and the sample size is small or moderate, provided the variable is not too far from being normally distributed. In other words, the t-interval procedure is robust to moderate violations of the normality assumption. When considering the t-interval procedure, it is also important to watch for outliers ...
Lecture 3
... If you care about wether a parameter is equal to a certain prespecified value, there is an alternative to hypothesis testing Just check whether the prespecified value is contained in the confidence ...
... If you care about wether a parameter is equal to a certain prespecified value, there is an alternative to hypothesis testing Just check whether the prespecified value is contained in the confidence ...
Chapter 18 - Dustin Tench
... t Test -OR- z Test? (Example 7 – Using the Calculator) An inventor has developed a new, energy-efficient lawn mower engine. He claims that the engine will run continuously for 5 hours (300 minutes) on a single gallon of regular gasoline. Suppose a simple random sample of 50 engines is tested. The e ...
... t Test -OR- z Test? (Example 7 – Using the Calculator) An inventor has developed a new, energy-efficient lawn mower engine. He claims that the engine will run continuously for 5 hours (300 minutes) on a single gallon of regular gasoline. Suppose a simple random sample of 50 engines is tested. The e ...
1017
... gene expression values are median centered, setting each gene’s median expression value to zero. the median absolute deviation (MAD) is calculated and scaled to 1 by dividing each gene expression value by its MAD. the 75th, 90th, and 95th percentiles of the transformed expression values are tabulate ...
... gene expression values are median centered, setting each gene’s median expression value to zero. the median absolute deviation (MAD) is calculated and scaled to 1 by dividing each gene expression value by its MAD. the 75th, 90th, and 95th percentiles of the transformed expression values are tabulate ...
9.1 Sampling Distributions (new)
... The value of a statistic varies from sample to sample when you are taking repeated random samples. The concept listed above is called ...
... The value of a statistic varies from sample to sample when you are taking repeated random samples. The concept listed above is called ...
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.