Notes 8
... allocation with a 10% sample would mean sampling 240 men and 160 women. For a stratified random sample with proportional allocation, the probability that an individual will be selected in the sample, n / N , is the same as in a simple random sample but many of the “bad” samples that could occur in a ...
... allocation with a 10% sample would mean sampling 240 men and 160 women. For a stratified random sample with proportional allocation, the probability that an individual will be selected in the sample, n / N , is the same as in a simple random sample but many of the “bad” samples that could occur in a ...
chapter 1
... Index number – nationwide average price (less variable than the price at any one store that may from time to time offer special prices) Seasonally adjusted – helps to avoid misinterpretation especially for short periods of time. Decomposing time series Statistical software programs can help to exami ...
... Index number – nationwide average price (less variable than the price at any one store that may from time to time offer special prices) Seasonally adjusted – helps to avoid misinterpretation especially for short periods of time. Decomposing time series Statistical software programs can help to exami ...
Statistics in Water Quality Research
... Mean is influenced by outliers Median is robust against (resistant to) outliers Mean “moves” toward outliers Median represents bulk of observations almost always ...
... Mean is influenced by outliers Median is robust against (resistant to) outliers Mean “moves” toward outliers Median represents bulk of observations almost always ...
11.1
... Significance level is the value (alpha) to which we compare our p-value in order to determine significance. “Statistically significant” = not likely to happen by chance If p-value < alpha = statistically significant. P-value allows us to assess significance at any level we choose. If you are going t ...
... Significance level is the value (alpha) to which we compare our p-value in order to determine significance. “Statistically significant” = not likely to happen by chance If p-value < alpha = statistically significant. P-value allows us to assess significance at any level we choose. If you are going t ...
sample.
... • If n is the sample size, n-1 is the “degrees of freedom” of the sample. • The general idea: Given n pieces of data, you start out with n degrees of freedom. However, when you compute the mean, you are left with only n-1 degrees of freedom, because the mean constrains the n samples. – Given the m ...
... • If n is the sample size, n-1 is the “degrees of freedom” of the sample. • The general idea: Given n pieces of data, you start out with n degrees of freedom. However, when you compute the mean, you are left with only n-1 degrees of freedom, because the mean constrains the n samples. – Given the m ...
PRCACalculus
... The properties for the sampling distribution of x1 – x2 implies that x1 – x2 can be standardized to obtain a variable with a sampling distribution that is approximately the standard normal (z) distribution. When two random samples are independently selected and n1 and n2 are both large or the popul ...
... The properties for the sampling distribution of x1 – x2 implies that x1 – x2 can be standardized to obtain a variable with a sampling distribution that is approximately the standard normal (z) distribution. When two random samples are independently selected and n1 and n2 are both large or the popul ...
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.