True or false? In an isosceles triangle all sides are unequal.
2 answers:
An isosceles triangle is a triangle with (no less than) two equivalent sides. In the figure beneath, the two equivalent sides have length and the staying side has length . This property is proportionate to two edges of the triangle being equivalent. An isosceles triangle in this way has both two equivalent sides and two equivalent points. The name gets from the Greek iso (same) and skelos (leg).
A triangle with all sides square with is called an equilateral triangle, and a triangle without any sides approach is known as a scalene triangle. An equilateral triangle is in this way an uncommon instance of an isosceles triangle having not only two, but rather each of the three sides and edges parallel. Another uncommon instance of an isosceles triangle is the isosceles right triangle.
A triangle with all sides square with is called an equilateral triangle, and a triangle without any sides approach is known as a scalene triangle. An equilateral triangle is in this way an uncommon instance of an isosceles triangle having not only two, but rather each of the three sides and edges parallel. Another uncommon instance of an isosceles triangle is the isosceles right triangle.
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Answer:
With outlier (102)
If we compare the p value and a significance level assumed we see that
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, so we can conclude that the true mean is not significantly higher than 125 feet at 5% of significance.
Without outlier (102)
And we conclude that we reject the null hypothesis since . So the final conclusion would be not use the method since the value of 102 observed can be a potential outlier, removing this value we see that we reject the null hypothesis and we have a significant result that the true mean is higher than 125.
Step-by-step explanation:
Previous concepts and data given
The margin of error is the range of values below and above the sample statistic in a confidence interval.
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
Data: 129, 128, 130, 132, 135, 123, 102, 125, 128, 130
We can calculate the mean with the following formulas:
represent the sample mean
represent the sample standard deviation
n=10 represent the sample selected
significance level
State the null and alternative hypotheses.
We need to conduct a hypothesis in order to check if the mean is significantly higher than 125, the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
If we analyze the size for the sample is < 30 and we don't know the population deviation so is better apply a t test to compare the actual mean to the reference value, and the statistic is given by:
(1)
t-test: "Is used to compare group means. Is one of the most common tests and is used to determine if the mean is (higher, less or not equal) to an specified value".
Calculate the statistic
We can replace in formula (1) the info given like this:
P-value
First we need to calculate the degrees of freedom given by:
Then since is a right tailed sided test the p value would be:
Conclusion
If we compare the p value and a significance level assumed we see that
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, so we can conclude that the true mean is not significantly higher than 125 feet at 5% of significance.
Using all the data we see that we don;t have enough info to conclude that the true mean is higher than 125, but if we see careful 9 of the 10 values are over the limit of 125 feet. And if we repeat the procedure with the outlier of 102. We got this:
represent the sample mean
represent the sample standard deviation
First we need to calculate the degrees of freedom given by:
Then since is a right tailed sided test the p value would be:
And we conclude that we reject the null hypothesis since . So the final conclusion would be not use the method since the value of 102 observed can be a potential outlier removing this value we see that we reject the null hypothesis and we have a significant result that the true mean is higher than 125.