PLAUSIBILITY OF MU = 7 FOR FLORIDIAN STUDENT NEWSPAPER READING HABITS?

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In summary, the FL Student Survey reports the mean number of times a week students read a newspaper to be 4.1, with a standard deviation of 3.0 and a 95% confidence interval of (3.325-4.875). The question asks if it is plausible for the population mean (mu) to equal 7, and while it is possible, it is not likely based on the calculated confidence interval.
  • #1
Fear_of_Math
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Hey ladies and gents,
I have a question that I'm not fully getting.

An FL Student Survey reprts the results for responses on the number of times a week the subject reads a newspaper:

Variable : news
n = 60
mean = 4.1
standard deviation = 3.0
SE mean = 0.387
95% CI = (3.325-4.875)

QUESTION: Is it plausible that mu = 7, where mu is the population mean for all Floridian Student? Explain.
I'm thinking yes, because confdence interval is an estimate of population proportion, and mu is the actual population mean. If this is true, then since we only have 95% confidence, mu could equal 7 because we have 5% that our estimates are incorrect and not between the interval. Yet, if the interval doesn't have 7 in it, how can this be true. I'm confused to this concept.

As always, the help is highly appreciated!
 
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  • #2
Fear_of_Math said:
Hey ladies and gents,
I have a question that I'm not fully getting.

An FL Student Survey reprts the results for responses on the number of times a week the subject reads a newspaper:

Variable : news
n = 60
mean = 4.1
standard deviation = 3.0
SE mean = 0.387
95% CI = (3.325-4.875)

QUESTION: Is it plausible that mu = 7, where mu is the population mean for all Floridian Student? Explain.
I'm thinking yes, because confdence interval is an estimate of population proportion, and mu is the actual population mean. If this is true, then since we only have 95% confidence, mu could equal 7 because we have 5% that our estimates are incorrect and not between the interval. Yet, if the interval doesn't have 7 in it, how can this be true. I'm confused to this concept.

As always, the help is highly appreciated!

I haven't checked the calculations, so I'm basing this response on the assumption that the endpoints of the interval were correctly calculated.

This interval is an estimate of the population mean , not the proportion as your first sentence states (I'll assume that was a typo). Based on this interval, is it likely that [tex] \mu = 7 [/tex]? No, it isn't, since 7 is not located in the interval.

Could the true mean be 7? Yes, but it isn't likely to be, for the reason stated above.
 
  • #3
Gracias!
And yes, meant to say mean, but it was 1 in the morning...
 

Related to PLAUSIBILITY OF MU = 7 FOR FLORIDIAN STUDENT NEWSPAPER READING HABITS?

What is a confidence interval?

A confidence interval is a range of values within which the true population parameter is likely to fall with a certain level of confidence. It is a statistical measure used to estimate the precision of a sample statistic.

How is a confidence interval calculated?

A confidence interval is calculated using a sample mean and a margin of error. The margin of error is determined by the sample size and the desired level of confidence, usually 95% or 99%. The formula for calculating a confidence interval is: Sample mean ± (Z-value * Standard Error)

What is the purpose of a confidence interval?

The purpose of a confidence interval is to provide a range of values within which the true population parameter is likely to fall. This allows us to make inferences about the population based on a sample.

What is the difference between a confidence interval and a prediction interval?

A confidence interval is used to estimate the true population parameter, while a prediction interval is used to make a prediction about an individual data point. A confidence interval is based on the sample mean, while a prediction interval takes into account both the sample mean and the variability of individual data points.

What factors affect the width of a confidence interval?

The width of a confidence interval is affected by the sample size, the level of confidence, and the variability of the data. A larger sample size, higher level of confidence, and lower variability will result in a narrower confidence interval, while a smaller sample size, lower level of confidence, and higher variability will result in a wider confidence interval.

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