Of all the visitors to a museum on Saturday, 18% were kids younger than 11 years old. In lowest terms, write the fraction of vis
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Answer:
The table a not represent a proportional relationship between the two quantities
The table b represent a proportional relationship between the two quantities
Step-by-step explanation:
we know that
A relationship between two variables, x, and y, represent a proportional variation if it can be expressed in the form or
<u><em>Verify each table</em></u>
<em>Table a</em>
Let
A ----> the independent variable or input value
B ----> the dependent variable or output value
the value of k will be
For A=35, B=92 --->
For A=23, B=80 --->
the values of k are different
therefore
There is no proportional relationship between the two quantities
<em>Table b</em>
Let
C ----> the independent variable or input value
D ----> the dependent variable or output value
the value of k will be
For C=20, D=8 --->
For C=12.5, D=5 --->
the values of k are equal
therefore
There is a proportional relationship between the two quantities
The linear equation is equal to
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
is the value that we want to test
represent the significance level
z would represent the statistic (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)
The One-Sample Proportion Test is used to assess whether a population proportion 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.
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
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% .