Sometimes, given a random variable X, you want to know the value, X= x such that the proportion of values that which are at least as extreme as x is a given percentage.
When testing for the location of the mean, m, of a normal distribution you may wish to be 95% sure that the mean lies within some interval. If the interval is symmetric about m, and Z(m) is the z score for the mean, then you require Prob(Z(l) < Z(m) < Z(u)) = 0.05. This establishes the lower and upper bounds for the interval. It is easier to convert these to the raw scores to determine the lower and upper bounds for x-bar for testing whether or not the sample mean is consistent with the hypothesised mean.
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To compute a z-score for the Beery Visual-Motor Integration (VMI) test, first obtain the raw score from the test. Then, use the mean and standard deviation of the normative sample for the Beery VMI to calculate the z-score using the formula: ( z = \frac{(X - \mu)}{\sigma} ), where ( X ) is the raw score, ( \mu ) is the mean, and ( \sigma ) is the standard deviation. The resulting z-score indicates how many standard deviations the raw score is from the mean of the normative population.
Yes.z = (raw score - mean)/standard error.Since the standard error is positive, z < 0 => (raw score - mean) < 0 => raw score < mean.
z score = (test score - mean score)/SD z score = (87-81.1)/11.06z score = 5.9/11.06z score = .533You can use a z-score chart to calculate the probability from there.
A z-score requires the mean and standard deviation (or standard error). There is, therefore, not enough information to answer the question.
There is not enough information to answer your question. To determine a Z-Score, the mean and standard deviation is also required.