Motorola used the normal distribution to determine the probability of defects and the number of defects expected in a production process. Assume production process produces items with a mean weight of 8 ounces. a. The process standard deviation is 0.10 ounces, and the process control is set at plus or minus 2.5 standard deviations. Units with weights less than 7.75 or greater than 8.25 ounces will be classified as defects. What is the probability of a defect (to 4 decimals)? In a production run of 1000 parts, how many defects would be found (round to the nearest whole number)? b. Through process design improvements, the process standard deviation can be reduced to 0.09 ounces. Assume the process control remains the same, with weights tess than 7.75 or greater than 8.25 ounces being classined as defects. What is the probabillty of a defect (round to 4 decimals; if necessary)? In a production run of 1000 parts, how many defects would be found (to the nearest whole number)? c. What is the adyantage of reducing process variation, thereby causing a problem limits to be at a greater number of standard deviations from the mean