The confidence interval is a range of values, derived from a statistical calculation, that is likely to contain an unknown population parameter. The width of the confidence interval gives us some idea about how uncertain we are about the unknown parameter. A wider interval may indicate that more data should be collected before anything very definite can be said about the parameter.

The confidence level is the percentage of all possible samples that can be expected to include the true population parameter. For example, a 90% confidence level implies that we would expect 90% of the interval estimates to include the population parameter; whereas a 99% confidence level implies that we would expect 99% of the interval estimates to include the population parameter.

In this case, the 90% confidence interval for the mean monthly family income is ($3800, $4200). This means that we are 90% confident that the true mean monthly family income lies between $3800 and $4200.

If the analyst had used a 99% confidence level instead, the confidence interval would be wider. This is because a higher confidence level means that we want to be more sure that we have captured the true population parameter, and so we need a wider interval to be more confident.

However, without knowing the standard deviation of the population or the sample, we cannot calculate the exact 99% confidence interval. But we can say that it would be wider than the 90% confidence interval of ($3800, $4200).

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The confidence interval is a range of values, derived from a statistical calculation, that is likely to contain an unknown population parameter. The width of the confidence interval gives us some idea about how uncertain we are about the unknown parameter. A wider interval may indicate that more data should be collected before anything very definite can be said about the parameter.

The confidence level is the percentage of all possible samples that can be expected to include the true population parameter. For example, a 90% confidence level implies that we would expect 90% of the interval estimates to include the population parameter; whereas a 99% confidence level implies that we would expect 99% of the interval estimates to include the population parameter.

In this case, the 90% confidence interval for the mean monthly family income is ($3800, $4200). This means that we are 90% confident that the true mean monthly family income lies between $3800 and $4200.

If the analyst had used a 99% confidence level instead, the confidence interval would be wider. This is because a higher confidence level means that we want to be more sure that we have captured the true population parameter, and so we need a wider interval to be more confident.

However, without knowing the standard deviation of the population or the sample, we cannot calculate the exact 99% confidence interval. But we can say that it would be wider than the 90% confidence interval of ($3800, $4200).

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The confidence interval is a range of values, derived from a statistical calculation, that is likely to contain an unknown population parameter. The width of the confidence interval gives us some idea about how uncertain we are about the unknown parameter. A wider interval may indicate that more data should be collected before anything very definite can be said about the parameter.

The confidence level is the percentage of all possible samples that can be expected to include the true population parameter. For example, a 90% confidence level implies that we would expect 90% of the interval estimates to include the population parameter; whereas a 99% confidence level implies that we would expect 99% of the interval estimates to include the population parameter.

In this case, the 90% confidence interval for the mean monthly family income is ($3800, $4200). This means that we are 90% confident that the true mean monthly family income lies between $3800 and $4200.

If the analyst had used a 99% confidence level instead, the confidence interval would be wider. This is because a higher confidence level means that we want to be more sure that we have captured the true population parameter, and so we need a wider interval to be more confident.

However, without knowing the standard deviation of the population or the sample, we cannot calculate the exact 99% confidence interval. But we can say that it would be wider than the 90% confidence interval of ($3800, $4200).

An analyst, using a random sample of n = 500 families, obtained a 90 per cent confidence interval for the mean monthly family income for a large population: ($3800, $4200). If the analyst had used a 99 per cent confidence level instead, the confidence interval would be:

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