Factorize: a^2+10ab -75b^2
2 answers:
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Answer:
The 95% confidence interval is
Step-by-step explanation:
From the question we are told that
The first sample size is
The first sample mean is
The first standard deviation is
The second sample size is
The second sample mean is
The second standard deviation is
Generally the degree of freedom is mathematically represented as
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=>
Generally the standard error is mathematically represented as
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From the question we are told the confidence level is 95% , hence the level of significance is
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Generally from the t distribution table the critical value of at a degree of freedom of is
Generally the margin of error is mathematically represented as
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Generally 95% confidence interval is mathematically represented as
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Answer:
Null hypothesis:
Alternative hypothesis:
A type of error I for this case would be reject the null hypothesis that the population proportion is lower or equal than 50 when actually is true.
So then the best option for this case is:
b. The researcher concludes that the mean amount of television watched by young men is greater than 50 (reject the null hypothesis), when the mean is really less than or equal to 50.
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
On this case we want to test if the mean amount of television watched daily by young men is greater than 50 minutes), so the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
A type of error I for this case would be reject the null hypothesis that the population proportion is lower or equal than 50 when actually is true.
So then the best option for this case is:
b. The researcher concludes that the mean amount of television watched by young men is greater than 50 (reject the null hypothesis), when the mean is really less than or equal to 50.