While conducting a test of modems being manufactured, it is found that 10 modems were faulty out of a random sample of 367 modems. The probability of obtaining this many bad modems (or more), under the assumptions of typical manufacturing flaws would be 0.013. Is this an unusually high number of faulty modems

We conclude that this is an unusually high number of faulty modems.

Step-by-step explanation:

We are given that while conducting a test of modems being manufactured, it is found that 10 modems were faulty out of a random sample of 367 modems.

The probability of obtaining this many bad modems (or more), under the assumptions of typical manufacturing flaws would be 0.013.

Let p = population proportion.

So, Null Hypothesis, : p = 0.013      {means that this is an unusually 0.013 proportion of faulty modems}

Alternate Hypothesis, : p > 0.013      {means that this is an unusually high number of faulty modems}

The test statistics that would be used here One-sample z-test for proportions;

                             T.S. =    ~  N(0,1)

where, = sample proportion faulty modems= = 0.027

           n = sample of modems = 367

So, the test statistics  =  

                                     =  2.367

The value of z-test statistics is 2.367.

Since, we are not given with the level of significance so we assume it to be 5%. Now at 5% level of significance, the z table gives a critical value of 1.645 for the right-tailed test.

Since our test statistics is more than the critical value of z as 2.367 > 1.645, so we have sufficient evidence to reject our null hypothesis as it will fall in the rejection region due to which we reject our null hypothesis.

Therefore, we conclude that this is an unusually high number of faulty modems.

u bout ugly as   a mf

step-by-step explanation: lol

y=3n + 1

step-by-step explanation:

this is wrong because it says the second number is one less and i added one instead