Z SCORE (FEMALE) Optimal Result: -2 - 2 SD. Interpret your laboratory results now. A Z-score is helpful in diagnosing secondary osteoporosis and is always used for children, young adults, women who are pre-menopausal, and men under age 50. Although you may have low bone density when you have your first test, your doctor cannot tell if you have You would use the following formula: Z-score = (the initial data point - mean)/standard deviation. Brunner uses an example of finding out how a student's test score of 90 compared with the scores his peers received, which are 75, 80, 85, 90, and 95. First, we find out the mean of this data set, which is 85. For example, you want to know if an eight year old's pitching speed is unusually good compared to his or her league. If the mean little league pitch speed is 30 mph with a standard deviation of 4 mph, is a 38 mph pitch unusual? 4 mph is an X-Score. You convert to a Z-Score with this formula: Z= (X-mu)/sigma So the Z-Score is Z= (38-30)/4=2 The The population mean. The population standard deviation. The sample mean. The sample size. Usually in stats, you don't know anything about a population, so instead of a Z score you use a T Test with a T Statistic. The major difference between using a Z score and a T statistic is that you have to estimate the population standard deviation. The 2. The column marked T score shows how your bone mineral density compares with women in their thirties, the peak bone density years. when it is highly unlikely that you would suffer a fracture. Scores of +1.0 are good. Numbers between +1 and - 1 show normal bone mineral density. Scores between -1 and -2.5 indicate Osteopenia (thin bones). 11. No. The z score is not 'the number of standard deviations'. Instead the z-score of a value is the number of standard deviations that value is above the mean. A z-score of 1.7 is 1.7 standard deviations above the mean. A z score of -1 is one standard deviation below the mean, and so on. This is not mere nitpicking, it's essential to Usually after the first few Ys, the variables become somewhat meaningless. The PCA score for any of the Xi is just it's coefficient in each of the Ys. In my earlier example, the score for X2 in the first principal component (Y1) is 1.76. The way PCA does this magic is by computing eigenvectors of the covariance matrix. UFee.

what does the z score represent