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strojnjashka [21]
1 year ago
8

The Bay Area Online Institute (BAOI) has set a guideline of 60 hours for the time it should take to complete an independent stud

y course. To see if the guideline needs to be changed and if the actual time taken to complete the course exceeds60 hours, 16 students are randomly chosen and the average time to complete the course was 68hours with a standard deviation of 20 hours. What inference can BAOI make about the time it takes to complete this course?
Mathematics
1 answer:
yarga [219]1 year ago
6 0

Answer:

 At the 5% level, BAOI can infer that the average time to complete does not exceeds 60 hours.

Step-by-step explanation:

From the question we are told that

   The  population mean is \mu  =  60 \ hr

    The sample size is  n  =  16

    The  sample mean is  \= x  =  68 \ hr

     The  standard deviation is  \sigma  =  20 \ hr

The  null hypothesis is  H_o  :  \mu  =  60

The  alternative H_a :  \mu >  60

Here we would assume the level of significance of this test to be  

         \alpha  =  5\%  =  0.05

Next we will obtain the critical value of the level of significance from the normal distribution table, the value is    Z_{0.05} =  1.645

  Generally the test statistics  is mathematically represented as

           t =  \frac{ \= x  - \mu}{  \frac{ \sigma }{\sqrt{n} } }

substituting values

           t =  \frac{  68  - 60 }{  \frac{ 20 }{\sqrt{16} } }

          t = 1.6

Looking at the value of t and  Z_{\alpha } we see that t< Z_{\alpha } hence we fail to reject the null hypothesis

   This means that there no sufficient evidence to conclude that it takes more than 60 hours to complete the course

So

   At the 5% level, BAOI can infer that the average time to complete does not exceeds 60 hours.

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Answer:

\alpha =0.0668

Step-by-step explanation:

Data given and notation  

The info given by the problem is:

n=25 the random sample taken

\mu =5000 represent the population mean

\sigma =100 represent the population standard deviation

The critical region on this case is \bar X so then if the value of \bar X \geq 4970 we fail to reject the null hypothesis. In other case we reject the null hypothesis

Null and alternative hypotheses to be tested  

We need to conduct a hypothesis in order to determine if the true mean is 5000, the system of hypothesis would be:  

Null hypothesis:\mu = 5000  

Alternative hypothesis:\mu \neq 5000  

Let's define the random variable X ="The compressive strength".

We know from the Central Limit Theorem that the distribution for the sample mean is given by:

\bar X \sim N(\mu , \frac{\sigma}{\sqrt{n}})

Find the probability of committing a type I error when H0 is true.

The definition for type of error I is reject the null hypothesis when actually is true, and is defined as \alpha the significance level.

So we can define \alpha like this:

\alpha= P(Error I)= P(\bar X

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Answer:

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Step-by-step explanation:

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