The Glen Valley Steel Company manufactures steelbars.If the production process is working properly,it turns out steel bars with mean length of at least 2.8 feet.The quality control manager wants to determine whether the production equipment needs to be adjusted. A sample of 25 bars is selected from the production line.The sample indicates a mean length of2.73 feet, with a sample standard deviation of 0.20 feet. Should adjustments be made to the equipment?Test at the 95% confidence level
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
The Glen Valley Steel Company manufactures steelbars.If the production process is working properly,it turns out steel bars with mean length of at least 2.8 feet.The quality control manager wants to determine whether the production equipment needs to be adjusted. A sample of 25 bars is selected from the production line.The sample indicates a mean length of2.73 feet, with a sample standard deviation of 0.20 feet. Should adjustments be made to the equipment?Test at the 95% confidence level
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