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WITCHER [35]
2 years ago
14

Knowing the population standard deviation, a 95% confidence interval infers that the population mean ___________. is between 0 a

nd 100% is within ±1.96 standard deviations of the sample mean is within ±1.96 standard errors of the sample mean is too large
Mathematics
1 answer:
nevsk [136]2 years ago
3 0

Answer:

within ±1.96 standard deviations of the sample mean

Step-by-step explanation:

A 95% confidence interval is found using the formula C = 1 - α, and some other stuff, but let's focus on that for now. Using the formula:

.95 = 1 - α

α = .05

If α = .05, that means a 2-sided confidence interval would be found using the sample mean and the Z-score Z(subscript α/2), or Z.₀₂₅ because α AKA .05 divided by 2 = .025. From there, you take this either to your calculator or a Z-table (or perhaps you have a chart that lists the common CI values), and see that for the area to be .025 beneath a standard normal curve, your Z value is ±1.96 ("plus or minus" because we're considering a 2-sided confidence interval).

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The USDA conducted tests for salmonella in produce grown in California. In an independent sample of 252 cultures obtained from w
Marat540 [252]

Answer:

 The decision rule is  

Fail to reject the null hypothesis

  The conclusion is  

There no sufficient evidence to show that the proportion of salmonella in the region’s water differs from the proportion of salmonella in the region’s wildlife

Step-by-step explanation:

From the question we are told that

   The first  sample size is n_1   =  252

    The number that tested positive is  k_1  =  18

     The second sample size is  n_2   =  476

     The number that  tested positive is  k_2 =  20

     The level of significance is  \alpha  = 0.01

Generally the first sample proportion is mathematically represented as

      \^ p _1 =  \frac{k_1 }{ n_1 }

=>    \^ p _1 =  \frac{18 }{ 252 }

=>    \^ p _1 = 0.071

Generally the second sample proportion is mathematically represented as

      \^ p _2 =  \frac{k_2 }{ n_2 }

=>    \^ p _2 =  \frac{20 }{ 476}

=>    \^ p _2 = 0.042

The  null hypothesis is            H_o  :  p_1 - p_2 = 0

The alternative hypothesis is  H_a :  p_1 - p_2 \ne 0

Generally the test statistics is mathematically represented

       z =  \frac{ \^ p_1 - \^ p_2  -  ( p_ 1 - p_2 )}{ \sqrt{\frac{\^ p_1 (1-\^ p_1)}{ n_1  } + \frac{\^ p_2 (1-\^ p_2)}{ n_2  }  } }

=>    z =  \frac{ 0.071 - 0.042  - 0 }{ \sqrt{\frac{0.071  (1-0.071)}{  252  } + \frac{0.042 (1-\^ 0.042)}{ 476  }  } }

=>    z =  1.56

From the z table  the area under the normal curve to the right corresponding to  1.56   is  

        P(Z >  1.56 ) =0.05938

Generally the p-value is mathematically represented as

         p-value =  2 * P(Z >  1.56 )

=>      p-value = 2 *  0.05938

=>      p-value = 0.1188

From the value obtained we see that   p-value  >  \alpha hence

  The decision rule is  

Fail to reject the null hypothesis

  The conclusion is  

There no sufficient evidence to show that the proportion of salmonella in the region’s water differs from the proportion of salmonella in the region’s wildlife

 

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