P-Value alpha risk set at 0.05 indicates a normal distribution. Throughout this site the following assumptions apply unless otherwise specified: The area under the curve equals all of the observations or measurements. The mean is used to define the central location in a normal data set and the median, mode, and mean are near equal. Therefore about 95% of the values recorded are between 87.00mm and 97.00mm. The measurements of 87.00mm and 97.00mm are two standard deviations away from the mean of 92.00mm. Approximately what percent of measurements are between 87.00mm and 97.00mm?Īnswer: C. Therefore 35-5 = 30 is the lower value and 35+5 = 40 is the upper value.Ī distribution of measurements for the length of widgets was found to have a mean of 92.0mm and a standard deviation of 2.50mm. The standard deviation is the square root of the variance and therefore = 5. 68% of the distribution (area under the curve) is about +/- 1 standard deviation from the mean.
If a normal distribution has a mean of 35 and a variance of 25, 68% of the distribution can be found between which two values?Īnswer: A. 95% of the distribution (area under the curve) is 1.96 standard deviations from the mean which can be estimated at 2.Therefore 75-20 = 55 is the lower value and 75+20 = 95 is the upper value. If a normal distribution has a mean of 75 and a standard deviation of 10, 95% of the distribution can be found between which two values?Īnswer: C. However, when the data does not meet the assumptions of normality the data will require a transformation to provide an accurate capability analysis. This distribution is frequently used to estimate the proportion of the process that will perform within specification limits or a specification limit (NOT control limits - recall that specification limits and control limits are different). Many natural occurring events and processes with "common cause" variation exhibit a normal distribution (when it does not this is another way to help identify "special cause"). Most Six Sigma projects will involve analyzing normal sets of data or assuming normality. Over time, upon making numerous calculations of the cumulative density function and z-scores, with these three approximations in mind, you will be able to quickly estimate populations and percentages of area that should be under a curve. in the above picture, the mean is assumed = 0.
These three figures are often referred to as the Empirical Rule or the 68-95-99.5 Rule as approximate representations population data within 1,2, and 3 standard deviations from the mean of a normal distribution. These three figures should be committed to memory if you are a Six Sigma GB/BB.