Suppose a simple random sample of size nequals64 is obtained from a population with mu equals 88 and sigma equals 8. ​(a) Describe the sampling distribution of x overbar. ​(b) What is Upper P (x overbar greater than 89.7 )​? ​(c) What is Upper P (x overbar less than or equals 85.7 )​? ​(d) What is Upper P (87.35 less than x overbar less than 90.5 )​?

Answers

Answer 1
Answer:

Answer:

a) \bar X \sim N (\mu, (\sigma)/(√(n)))

With:

\mu_(\bar X)= 88

\sigma_(\bar X)= 8

b) z=(89.7-88)/((8)/(√(64)))= 1.7

P(Z>1.7) = 1-P(Z<1.7) =1-0.955=0.0446

c) z =(85.7-88)/((8)/(√(64)))= -2.3

P(Z<-2.3) = 0.0107

d) z =(87.35-88)/((8)/(√(64)))= -0.65

z =(90.5-88)/((8)/(√(64)))= 2.5

P(-0.65<z<2.5)=P(Z<2.5)-P(Z<-0.65) =0.994-0.258 = 0.736

Step-by-step explanation:

For this case we know the following propoertis for the random variable X

\mu = 88, \sigma = 8

We select a sample size of n = 64

Part a

Since the sample size is large enough we can use the central limit distribution and the distribution for the sample mean on this case would be:

\bar X \sim N (\mu, (\sigma)/(√(n)))

With:

\mu_(\bar X)= 88

\sigma_(\bar X)= 8

Part b

We want this probability:

P(\bar X>89.7)

We can use the z score formula given by:

z = (\bar X -\mu)/((\sigma)/(√(n)))

And if we find the z score for 89.7 we got:

z=(89.7-88)/((8)/(√(64)))= 1.7

P(Z>1.7) = 1-P(Z<1.7) =1-0.955=0.0446

Part c

P(\bar X<85.7)

We can use the z score formula given by:

z = (\bar X -\mu)/((\sigma)/(√(n)))

And if we find the z score for 85.7 we got:

z =(85.7-88)/((8)/(√(64)))= -2.3

P(Z<-2.3) = 0.0107

Part d

We want this probability:

P(87.35 <\bar X< 90.5)

We find the z scores:

z =(87.35-88)/((8)/(√(64)))= -0.65

z =(90.5-88)/((8)/(√(64)))= 2.5

P(-0.65<z<2.5)=P(Z<2.5)-P(Z<-0.65) =0.994-0.258 = 0.736


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Answers

Answer:

The 90% confidence interval is  0.199 <  p < 0.261

The sample size to develop a 95% confidence interval is n = 2032  

Step-by-step explanation:

From the question we are told that

   The sample size is n =500

    The sample proportion is  \^ p = 0.23

From the question we are told the confidence level is  90% , hence the level of significance is    

      \alpha = (100 - 90 ) \%

=>   \alpha = 0.10

Generally from the normal distribution table the critical value  of  (\alpha )/(2) is  

   Z_{(\alpha )/(2) } =  1.645

Generally the margin of error is mathematically represented as  

     E =  Z_{(\alpha )/(2) } * \sqrt{(\^ p (1- \^ p))/(n) }

=>   E =  1.645 * \sqrt{(0.23 (1- 0.23))/(500) }

=>   E =  0.03096

Generally 90% confidence interval is mathematically represented as  

      \^ p -E <  p <  \^ p +E

=>    0.23  -0.03096  <  p < 0.23  +  0.03096

=>   0.199 <  p < 0.261

From the question we are told the confidence level is  95% , hence the level of significance is    

      \alpha = (100 - 95 ) \%

=>   \alpha = 0.05

Generally from the normal distribution table the critical value  of  (\alpha )/(2) is  

   Z_{(\alpha )/(2) } =  1.96

The margin of error is given as E = 0.01

Generally the sample size is mathematically represented as  

    n = [\frac{Z_{(\alpha )/(2) }}{E} ]^2 * \^ p (1 - \^ p )

=>    n = [(1.96 )/(0.01) ]^2 *0.23 (1 - 0.23 )      

=>    n = 2032  

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