Answer:
 
  
Step-by-step explanation:
1) Data given and notation 
n=100 represent the random sample taken
X=18 represent the ounce cups of coffee that were underfilled
 estimated proportion of ounce cups of coffee that were underfilled
 estimated proportion of ounce cups of coffee that were underfilled
 is the value that we want to test
 is the value that we want to test
 represent the significance level
 represent the significance level  
z would represent the statistic (variable of interest)
 represent the p value (variable of interest)
 represent the p value (variable of interest)  
2) Concepts and formulas to use  
We need to conduct a hypothesis in order to test the claim that the true proportion it's higher than 0.1 or 10%:  
Null hypothesis: 
  
Alternative hypothesis: 
  
When we conduct a proportion test we need to use the z statistic, and the is given by:  
 (1)
 (1)  
The One-Sample Proportion Test is used to assess whether a population proportion  is significantly different from a hypothesized value
 is significantly different from a hypothesized value  .
.
3) Calculate the statistic  
Since we have all the info requires we can replace in formula (1) like this:  
 
  
4) Statistical decision  
It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.  
The significance level assumed for this case is  . The next step would be calculate the p value for this test.
. The next step would be calculate the p value for this test.  
Since is a one right tailed test the p value would be:  
 
  
So the p value obtained was a very low value and using the significance level given  we have
 we have  so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance thetrue proportion is not significanlty higher than 0.1 or 10% .
 so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance thetrue proportion is not significanlty higher than 0.1 or 10% .