Past data indicate that the variance of measurements made on sheet metal stampings by experienced quality control inspectors is 0.18 (inch)2. Such measurements made by an inexperienced inspector could have too large a variance (perhaps because of inability to read instruments properly) or too small a variance (perhaps because unusually high or low measurements are discarded). If a new inspector measures 101 stampings with variance of 0.13 (inch)2, test at the 0.05 level of significance whether the inspector is making satisfactory measurements. Assume data is normally distributed.
Past data indicate that the variance of measurements made on sheet metal stampings by experienced quality control inspectors is 0.18 (inch)2. Such measurements made by an inexperienced inspector could have too large a variance (perhaps because of inability to read instruments properly) or too small a variance (perhaps because unusually high or low measurements are discarded). If a new inspector measures 101 stampings with variance of 0.13 (inch)2, test at the 0.05 level of significance whether the inspector is making satisfactory measurements. Assume data is normally distributed.
Past data indicate that the variance of measurements made on sheet metal stampings by experienced quality control inspectors is 0.18 (inch)2. Such measurements made by an inexperienced inspector could have too large a variance (perhaps because of inability to read instruments properly) or too small a variance (perhaps because unusually high or low measurements are discarded). If a new inspector measures 101 stampings with variance of 0.13 (inch)2, test at the 0.05 level of significance whether the inspector is making satisfactory measurements. Assume data is normally distributed.
Past data indicate that the variance of measurements made on sheet metal stampings by experienced quality control inspectors is 0.18 (inch)2. Such measurements made by an inexperienced inspector could have too large a variance (perhaps because of inability to read instruments properly) or too small a variance (perhaps because unusually high or low measurements are discarded). If a new inspector measures 101 stampings with variance of 0.13 (inch)2, test at the 0.05 level of significance whether the inspector is making satisfactory measurements. Assume data is normally distributed.
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
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