Acoffee shop owner claims that more than 80% of coffee drinkers think that taste of a shops coffee is very important in determining where they purchase their coffee. in a random sample of 36 coffee drinkers, 31 think that the taste of a shops coffee is very important in determining where they purchase their coffee.
1. at α = 0.10, is there enough evidence to support the owners claim?

If we compare the p value obtained and the significance level given we see that so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 10% of significance the proportion of coffee drinkers  who  think that the taste of a shops coffee is very important in determining where they purchase their coffee is not significantly higher than 0.8 or 80%.

Step-by-step explanation:

Data given and notation

n=36 represent the random sample taken

X=31 represent the coffee drinkers  who  think that the taste of a shops coffee is very important in determining where they purchase their coffee.

estimated proportion of coffee drinkers  who  think that the taste of a shops coffee is very important in determining where they purchase their coffee.

is the value that we want to test

represent the significance level

Confidence=90% or 0.90

z would represent the statistic (variable of interest)

represent the p value (variable of interest)  

Concepts and formulas to use  

We need to conduct a hypothesis in order to test the true proportion is higher than 0.8:  

Null hypothesis:  

Alternative hypothesis:  

When we conduct a proportion test we need to use the z statistic, and the is given by:  

(1)  

The One-Sample Proportion Test is used to assess whether a population proportion is significantly different from a hypothesized value .

Calculate the statistic  

Since we have all the info requires we can replace in formula (1) like this:  

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 provided . The next step would be calculate the p value for this test.  

Since is a right tailed test the p value would be:  

If we compare the p value obtained and the significance level given we see that so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 10% of significance the proportion of coffee drinkers  who  think that the taste of a shops coffee is very important in determining where they purchase their coffee is not significantly higher than 0.8 or 80%.

17   x 256=4352

sorry if this is wrong, i'm new.

step-by-step explanation: