stats test 2

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When the sample size increases, everything else remaining the same, the width of a confidence interval for a population parameter will:

Decrease

As the margin of error decreases, the width of the confidence interval

Decreases

Confidence intervals always contain the true population value.

False

You have sampled 25 randomly selected students to find the mean test score. A 95% confidence interval for the mean came out to be between 85 and 92. Which of the following statements gives a valid interpretation of this interval?

If this procedure were to be repeated many times, 95% of the confidence intervals found would contain the true mean score.

If the standard deviation of a sample increases and you want the confidence interval width to remain the same, then the sample size must

Increase

In the construction of a confidence interval, as the confidence level required in estimating the mean increases, the width of the confidence interval

Increases

Which of the following is an advantage of confidence interval estimate over a point estimate for a population parameter?

Interval estimates take into account the fact that the statistic being used to estimate the population parameter is a random variable

When using the same data, the width of a confidence interval will be:

Narrower for 90% confidence than 95% confidence.

Which of the following are reasons that a sample may be a biased or unreliable sample. Choose all that apply.

People who were asked refused to answer. Trying to conclude that there is a cause-and-effect relationship when something else causes both. The funders of the project are partial to the results. The graphs are drawn in a way to mislead the reader.

Which of the following are reasons that a sample may be a biased or unreliable sample.

The sample is not representative of the population. Self-Selected Sample. (Voluntary Sample) The sample size is too small. The wording of survey question influences the response.

As the confidence level increases the margin of error will increase.

True

When the level of confidence and sample standard deviation remain the same, a confidence interval for a population mean based on a sample of n = 100 will be narrower than a confidence interval for a population mean based on a sample of n = 50.

True

The mean of the sampling distribution of means of all possible random samples size n is:

equal to the mean of the population.

A sampling distribution is made up of 1 sample.

false

As the confidence level decreases the confidence interval will get wider.

false

As the confidence level increases the confidence interval will get narrower

false

As the sample size increases the margin of error will increase.

false

As the sample size increases, the standard error gets larger

false

As the sample size increases, the standard error gets larger.

false

Calculating standard error of a sampling distribution is not important, we only need to look at the regular standard deviation of a single data set.

false

Confidence intervals always contain the population mean or proportion

false

It doesn't matter if the population is skewed, the distribution of sample means will always be bell shaped.

false

Random is not important in the central limit theorem.

false

Random samples should have at least 10 successes and at least 10 failures if we want our random sample percent to estimate the population percent. This means we will always need a data set size of exactly 20.

false

Random samples will be exactly the same as the population value.

false

Random samples will be guaranteed close to the population value.

false

The CLT only applies to sample means and does not apply to the distribution of sample percentages.

false

The average (center) of all the random sample percentages (proportions) will be a poor estimate of the population percent.

false

The sample mean from 1 sample is usually a more accurate estimate of the population mean than the center of a sampling distribution.

false

The sample percentage from just a single data set is usually much closer to the population percentage than the center of a sampling distribution of sample percentages.

false

The standard error of a sampling distribution and the standard deviation of a single data set are the same thing.

false

We want random samples to have at least 10 successes and at least 10 failures if we want our random sample percent to estimate the population percent. This means we will always need a data set size of exactly 20 (for proportions).

false

A 1993 survey conducted by the local paper in Kansas City, Missouri, one week before election day asked voters who they would vote for in the City Attorney's race. Thirty-seven percent said they would for the Democratic candidate. On election day, 41% actually did vote for the Democratic candidate. The 41% is a

parameter

A phone-in poll conducted by a newspaper reported that 64% of those who called in watched the TV show South Park on Comedy Central. The unknown true percentage of American citizens who watch South Park is a

parameter.

A phone-in poll conducted by a newspaper reported that 64% of those who called in watched the TV show South Park on Comedy Central. The number 64% is a(n)

statistic.

The sampling distribution of a statistic is

the distribution of values taken by a statistic in all possible samples of the same size from the same population.

The distribution of the values taken on by a statistic in all possible samples from the same population is called

the sampling distribution

The variability of a statistic is described by

the spread of its sampling distribution.

As the confidence level increases the margin of error will increase.

true

As the sample size increases the margin of error will decrease.

true

Even if a sample is random, the sample value may not be exactly the same as the population value.

true

If we have samples less than 30, we can still use the sample if the sample is nearly normal if we want our random sample means to estimate the population means.

true

It is possible to estimate the standard error from a sampling distribution instead of using the formula

true

Random samples should be at least 30 or nearly normal if we want to use the mean of our random sample to estimate the population means.

true

Random samples will often give very different sample means and sample percentages.

true

The average (center) of all the random sample means will be a good estimate of the population mean.

true

The average (center) of all the random sample percentages (proportions) will be a good estimate of the population percent.

true

The concepts of 95% confidence, confidence intervals, standard error and margin of error can all be better understood from studying sampling distributions.

true

The distribution of random sample means is normally distributed for sufficiently large samples.

true

The margin of error is usually larger than the standard error.

true

The sample percentage from the center of a sampling distribution of sample percentages is much closer to the actual percentage than from a single data set, usually.

true

To create a sampling distribution, take lots of random samples, calculate all their sample means or sample percentages and then make a graph of all the sample means or sample percentages.

true

We want our random samples of size at least 30 if we want our random sample means to estimate the population means.

true

When constructing a confidence interval for a sample proportion, you should use which distribution?

z- distribution


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