Probability Quiz True/False IE 415
Terms in this set (10)
Random variables only take on non-negative values
If a set of data represent realizations of some random variable, the "shape" of the histogram constructed from the data is an estimate of the shape of the cumulative distribution function of the random variable.
If x1, x2, .... xn represent n independent observations of some random variable X, then as n gets larger, X gets "closer" to the true mean E[X].
The information used to complete a hypothesis test (at significance level alpha), can also be used to construct a (1-alpha)*100% confidence interval.
When conduction a hypothesis test on the mean of a normally distributed random variable where the standard deviation is estimated from the sample data, the sampling distribution of the test statistics is a standard normal distribution.
The central limit theorem implies the sampling distribution of the sample standard deviation of a large (>30) number of observations is approximately normal.
In a hypothesis test Type II error is always larger than Type I error.
If x1, x2, ... xn represent n independent observations from some distribution and n>30, then the sample average x is an observation from an approximately normal distribution.
The sample average as a measure of central tendency is less sensitive to the presence of extreme or outlier values than the sample median.
Statistics computed from realizations of random variables are also realizations of a random variable.
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