Chapter Nine

1. What factors determine bias and variability of statistics?

The color of the data being analyzed

It's unbiased if the mean is equal to the true value of the parameter being estimated.
The variability of a statistic is described by the spread of its sampling distribution, its design and size.

The font size of the statistical report

The time of day the statistics are calculated

Bias and variability in statistics are determined by factors such as how closely the mean of the sample matches the true value of the population parameter being estimated, as well as the spread of the sampling distribution, the design of the study, and the size of the sample.

Explanation

Bias and variability in statistics are determined by factors such as how closely the mean of the sample matches the true value of the population parameter being estimated, as well as the spread of the sampling distribution, the design of the study, and the size of the sample.

2. What is the mean of a Sampling Distribution for a Proportion?

0.5

Mean=p

P^2

2*p

The mean of a Sampling Distribution for a Proportion is equal to the population proportion itself, denoted as 'p'.

Explanation

The mean of a Sampling Distribution for a Proportion is equal to the population proportion itself, denoted as 'p'.

3. Condition for Using Normal Approximation of a Sampling Distribution for a Proportion.

Does not allow for statistical inference.

Ensures that normal calculations are accurate enough for most statistical purposes.

Ensures that outliers are included in the analysis.

Helps in detecting outliers and anomalies in the data.

Requires the sample size to be extremely small.

The condition for using normal approximation of a sampling distribution for a proportion is to ensure that normal calculations are accurate enough for most statistical purposes. This allows for reliable statistical inference based on the sample data collected.

Explanation

The condition for using normal approximation of a sampling distribution for a proportion is to ensure that normal calculations are accurate enough for most statistical purposes. This allows for reliable statistical inference based on the sample data collected.

4. What are the characteristics of the Mean and Standard Deviation of a Sampling Distribution for a Mean?

Mean=population median

Mean=0

Standard deviation=0

Mean=population mean

In a Sampling Distribution for a mean, the mean of the sampling distribution is equal to the population mean, not zero or population median. The standard deviation of a sampling distribution for a mean is not necessarily zero.

Explanation

In a Sampling Distribution for a mean, the mean of the sampling distribution is equal to the population mean, not zero or population median. The standard deviation of a sampling distribution for a mean is not necessarily zero.

5. When can we use Normal Approximation of Sampling Distribution for a Mean?

B) The sampling distribution is only normal if the population distribution is skewed.

The sampling distribution is normal if the population is normal and also if it follows the central limit theorem, which is when there are more than 40 observations, the sampling distribution is normal.

A) The sampling distribution is always normal regardless of the population distribution.

C) The sampling distribution is normal only if the sample size is less than 40.

To use the Normal Approximation of Sampling Distribution for a Mean, the population should be normal or follow the Central Limit Theorem with more than 40 observations. Without meeting these criteria, the sampling distribution may not be normal.

Explanation

To use the Normal Approximation of Sampling Distribution for a Mean, the population should be normal or follow the Central Limit Theorem with more than 40 observations. Without meeting these criteria, the sampling distribution may not be normal.