Location of Packet 1
... variability - thus, improving the precision of inference Moving from descriptive statistics to making inference: Margin of Error (ME). ME allows statement about the range of plausible values for the population parameter. ME measures sampling variability you’d expect in repeated samples. Mathematical ...
... variability - thus, improving the precision of inference Moving from descriptive statistics to making inference: Margin of Error (ME). ME allows statement about the range of plausible values for the population parameter. ME measures sampling variability you’d expect in repeated samples. Mathematical ...
Estimating with Confidence
... The last chapter provided practice finding confidence intervals and carrying out tests of significance in a somewhat unrealistic setting. We needed the population σ. In reality σ and μ are rarely known. The conditions for inference about a mean are as before: SRS and a normal distribution. For these ...
... The last chapter provided practice finding confidence intervals and carrying out tests of significance in a somewhat unrealistic setting. We needed the population σ. In reality σ and μ are rarely known. The conditions for inference about a mean are as before: SRS and a normal distribution. For these ...
10-04 lecture
... • Are CU undergrads smarter than population? – Sample size n = 100, sample mean M = 103 ...
... • Are CU undergrads smarter than population? – Sample size n = 100, sample mean M = 103 ...
Estimating Means and Proportions
... same as the mean of the population from which the sample is drawn. • Variance: The variance of this distribution is equal to the population variance divided by the sample size (n). • Justification: Central Limit Theorem. • Implications: If we draw a random sample of sufficient size we can estimate t ...
... same as the mean of the population from which the sample is drawn. • Variance: The variance of this distribution is equal to the population variance divided by the sample size (n). • Justification: Central Limit Theorem. • Implications: If we draw a random sample of sufficient size we can estimate t ...
View/Open - Pan Africa Christian University
... The following data represent scores of 50 students in a calculus test. ...
... The following data represent scores of 50 students in a calculus test. ...
Refreshing Your Skills 11
... Recall that deviations are the signed differences between the data values and the mean. The standard deviation, s, is the sum of the squares of the deviations divided by one less than the number of data values. ...
... Recall that deviations are the signed differences between the data values and the mean. The standard deviation, s, is the sum of the squares of the deviations divided by one less than the number of data values. ...
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.