Statistics: 1.1 Paired t-tests
... For this test to be valid the differences only need to be approximately normally distributed. Therefore, it would not be advisable to use a paired t-test where there were any extreme outliers. Example Using the above example with n = 20 students, the following results were obtained: Student Pre-modu ...
... For this test to be valid the differences only need to be approximately normally distributed. Therefore, it would not be advisable to use a paired t-test where there were any extreme outliers. Example Using the above example with n = 20 students, the following results were obtained: Student Pre-modu ...
Part I In the Beginning
... “S” (sigma) tells you to add together whatever follows it X is each individual score in the group The n is the sample size ...
... “S” (sigma) tells you to add together whatever follows it X is each individual score in the group The n is the sample size ...
Statistics MATH-1410 Mean and Standard Deviation of Discrete Random Variables
... We can now use the completed probability distribution to determine the mean (or the expected value) of the random variable. We are fortunate in this problem since we have access to the raw data. We know, for example, that there were 38 households with exactly one television. Whenever we have raw dat ...
... We can now use the completed probability distribution to determine the mean (or the expected value) of the random variable. We are fortunate in this problem since we have access to the raw data. We know, for example, that there were 38 households with exactly one television. Whenever we have raw dat ...
Math 116 – Activity 1 – Part 1 - Montgomery College Student Web
... e) The 75th percentile of the CRGVL distribution is about __________ which is the same as the _________th percentile of the PRGVL distribution. ...
... e) The 75th percentile of the CRGVL distribution is about __________ which is the same as the _________th percentile of the PRGVL distribution. ...
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.