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Answer:
The probability that the sample mean would differ from the population mean by more than 0.5 millimeters is 0.2420.
Step-by-step explanation:
Let <em>X</em> = the diameter of the steel bolts manufactured by the steel bolts manufacturing company Thompson and Thompson.
The mean diameter of the bolts is:
<em>μ</em> = 149 mm.
The standard deviation of the diameter of bolts is:
<em>σ</em> = 5 mm.
A random sample, of size <em>n</em> = 49, of steel bolts are selected.
The population of the diameter of bolts is not known.
According to the Central Limit Theorem if we have a population with mean <em>μ</em> and standard deviation <em>σ</em> and we take appropriately huge random samples (<em>n</em> ≥ 30) from the population with replacement, then the distribution of the sample mean will be approximately normally distributed.
The mean of the sampling distribution of sample mean is:
The standard deviation of the sampling distribution of sample mean is:
Compute the probability that the sample mean would differ from the population mean by more than 0.5 millimeters as follows:
*Use a <em>z</em>-table for the probability.
Thus, the probability that the sample mean would differ from the population mean by more than 0.5 millimeters is 0.2420.