a manufacturing plant finds that 4 out of every 335 items it makes has a defect. let x represent yhe number of items made. enter
1 answer:
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Answer:
Length: 7
Width: 4
Step-by-step explanation:
We can create a system of equations for this problem, where is the width and
is the length.
The perimeter of a rectangle is twice its length added to twice its width.
The length is 3 more than the width:
We can now substitute in as
in the equation
.
Distribute the first terms:
Combine like terms:
Subtract 6 from both sides:
Divide both sides by 4:
Now we know that w = 4. We can now substitute this inside an equation to find .
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Answer:
So the p value obtained was a very high value and using the significance level given we have
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 5% of significance the proportion of girls born is not significantly different from 0.467
Step-by-step explanation:
Data given and notation
n=150 represent the random sample taken
X=63 represent the number of girls born
estimated proportion of girls born
is the value that we want to test
represent the significance level
Confidence=95% or 0.95
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 claim that the true proportion if girls is 0.467.:
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 bilateral test the p value would be:
So the p value obtained was a very high value and using the significance level given we have
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 5% of significance the proportion of girls born is not significantly different from 0.467