RPQP27 - cucet 2017
... 27. A bag contains 3 white and 5 red balls. A game is played in which a ball is drawn, its colour is noted and replaced with two additional balls of the same colour. The selection is made 3 times. Then what is the probability that a white ball is selected at each trial ? A) 21/44 B) 7/64 C) 9/320 D) ...
... 27. A bag contains 3 white and 5 red balls. A game is played in which a ball is drawn, its colour is noted and replaced with two additional balls of the same colour. The selection is made 3 times. Then what is the probability that a white ball is selected at each trial ? A) 21/44 B) 7/64 C) 9/320 D) ...
Chapter 8 Sampling Distributions – Sample
... 3) A soft-drink bottle vendor claims that its production process yields bottles with a mean internal strength of 157 psi (pounds per square inch) and a standard deviation of 3 psi and is normally distributed. As part of its vendor surveillance, a bottler strikes an agreement with the vendor that per ...
... 3) A soft-drink bottle vendor claims that its production process yields bottles with a mean internal strength of 157 psi (pounds per square inch) and a standard deviation of 3 psi and is normally distributed. As part of its vendor surveillance, a bottler strikes an agreement with the vendor that per ...
Standardizing a Normal sampling distribution
... A sample size of 40 or more will typically be good enough to overcome an extremely skewed population and mild (but not extreme) outliers in the sample. In many cases, n = 25 isn’t a huge sample. Thus, even for strange population distributions we can assume a Normal sampling distribution of the sampl ...
... A sample size of 40 or more will typically be good enough to overcome an extremely skewed population and mild (but not extreme) outliers in the sample. In many cases, n = 25 isn’t a huge sample. Thus, even for strange population distributions we can assume a Normal sampling distribution of the sampl ...
Data Analysis
... Estimated range within which population mean falls – e.g., 95% confidence interval of mean, based on our sample, is (1.57 8.77) where = population mean – We are 95% confident true mean of population (from which our sample was drawn) lies within this range ...
... Estimated range within which population mean falls – e.g., 95% confidence interval of mean, based on our sample, is (1.57 8.77) where = population mean – We are 95% confident true mean of population (from which our sample was drawn) lies within this range ...
Section 8-1
... population parameter • Margin of error – MOE: critical value times standard error of the estimate; the • Critical Values – a value from z or t distributions corresponding to a level of confidence C • Level C – area between +/- critical values under the given test curve (a normal distribution or t-di ...
... population parameter • Margin of error – MOE: critical value times standard error of the estimate; the • Critical Values – a value from z or t distributions corresponding to a level of confidence C • Level C – area between +/- critical values under the given test curve (a normal distribution or t-di ...
Chapter 4 - Confidence Interval
... To compute a confidence interval, we will consider two situations: i. We use sample data to estimate, with X and the population standard deviation is known. ii. We use sample data to estimate, with X and the population standard deviation is unknown. In this case, we substitute the sample st ...
... To compute a confidence interval, we will consider two situations: i. We use sample data to estimate, with X and the population standard deviation is known. ii. We use sample data to estimate, with X and the population standard deviation is unknown. In this case, we substitute the sample st ...
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.