The first step in simplifying a rational expression is to.
1 answer:
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Answer:
It does not appear that, as a group, the students are reasonably good at estimating one minute.
Step-by-step explanation:
We are given the following data in the question:
75, 88, 51, 73, 49, 31, 69, 74, 72, 59, 72, 81, 99, 101, 73
Formula:
where are data points,
is the mean and n is the number of observations.
Sum of squares of differences = 4739.733
Population mean, μ = 60 minutes
Sample mean, = 71.13 minutes
Sample size, n = 15
Alpha, α = 0.10
Sample standard deviation, s = 18.39 minutes
First, we design the null and the alternate hypothesis
We use Two-tailed t test to perform this hypothesis.
Formula:
Putting all the values, we have
Calculating the p-value from the table, we have,
P-value = 0.034354
Since the p-value is lower than the significance level, we fail to accept the null hypothesis and reject it. We accept the alternate hypothesis.
Thus, we conclude that it does not appear that, as a group, the students are reasonably good at estimating one minute.
Answer:
The system of hypothesis for this case are:
Null hypothesis:
Alternative hypothesis:
A type of error I for this case would be reject the null hypothesis that the true mean is equal than 150 when actually is not true.
Step-by-step explanation:
Previous concepts
A hypothesis is defined as "a speculation or theory based on insufficient evidence that lends itself to further testing and experimentation. With further testing, a hypothesis can usually be proven true or false".
The null hypothesis is defined as "a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove".
The alternative hypothesis is "just the inverse, or opposite, of the null hypothesis. It is the hypothesis that researcher is trying to prove".
Type I error, also known as a “false positive” is the error of rejecting a null hypothesis when it is actually true. Can be interpreted as the error of no reject an alternative hypothesis when the results can be attributed not to the reality.
Type II error, also known as a "false negative" is the error of not rejecting a null hypothesis when the alternative hypothesis is the true. Can be interpreted as the error of failing to accept an alternative hypothesis when we don't have enough statistical power.
Solution to the problem
The system of hypothesis for this case are:
Null hypothesis:
Alternative hypothesis:
A type of error I for this case would be reject the null hypothesis that the true mean is equal than 150 when actually is not true.