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The operation manager at a tire manufacturing company believes that the mean mileage of a tire is 44,663 miles, with a standard deviation of 2594 miles. What is the probability that the sample mean would differ from the population mean by less than 199 miles in a sample of 209 tires if the manager is correct?

Answer :

Answer:

The probability is  [tex]P(| \= X- \mu| < 199 ) = 0.733[/tex]

Step-by-step explanation:

From the question we are told that

   The mean mileage of a tire is   [tex]\mu = 44663[/tex]

   The standard deviation is  [tex]\sigma = 2594[/tex]

   The sample size is  n  =  209

 Generally the standard error of mean is mathematically represented as

    [tex]\sigma_{x}  =  \frac{\sigma}{\sqrt{n} } [/tex]

=>  [tex]\sigma_{x}  =  \frac{2594}{\sqrt{209} } [/tex]  

=>  [tex]\sigma_{x}  =   179.4  [/tex]      

Generally  the probability that the sample mean would differ from the population mean by less than 199 miles  is mathematically represented as

     [tex]P(| \= X- \mu| < 199 ) = P(|\frac{\= X - \mu}{ \sigma_{x}}| < \frac{199}{ 179.4})[/tex]

[tex]\frac{\= X -\mu}{\sigma }  =  Z (The  \ standardized \  value\  of  \ \= X )[/tex]

=>  [tex]P(| \= X- \mu| < 199 ) = P(|Z| < \frac{199}{ 179.4})[/tex]

=>   [tex]P(| \= X- \mu| < 199 ) = P(|Z|< 1.11)[/tex]

=>   [tex]P(| \= X- \mu| < 199 ) = P(-1.11 \le Z \le 1.11 )[/tex]

=>   [tex]P(| \= X- \mu| < 199 ) = P(Z \le 1.11 ) - P( Z \le -1.11 )[/tex]

From the z  table  the area under the normal curve to the left corresponding to 1.11 and -1.11 is  

        [tex]P(Z \le 1.11 ) = 0.8665[/tex]

and

       [tex]P(Z \le - 1.11 ) = 0.1335[/tex]

So

    [tex]P(| \= X- \mu| < 199 ) = 0.8665 - 0.1335[/tex]

=>  [tex]P(| \= X- \mu| < 199 ) = 0.733[/tex]

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