Study guides

☆☆

Q: How do you find the point estimate of the population mean?

Write your answer...

Submit

Related questions

A point estimate of a population parameter is a single value of a statistic. For example, the sample mean x is a point estimate of the population mean μ. Similarly, the sample proportion p is a point estimate of the population proportion P.

Nearly true. It is a point estimate, not point ofestimate.

No. The average of a dataset is the point estimate for the mean of the population.

he population mean

You add together all the observed values and divide the answer by the number of observations.

The population mean is the mean calculated over every member of the set of subjects being studied. It is usually not available and a survey is used to find an estimate for the population mean. The mean value of the variable in question, calculated from only the subjects included in the sample (or survey) is the sample mean. Provided some basic statistical requirements are met, the sample mean is a "good" estimate of the population mean.

The best point estimator of the population mean would be the sample mean.

You calculate the actual sample mean, and from that number, you then estimate the probable mean (or the range) of the population from which that sample was drawn.

You can estimate the median and the mean.

The variance decreases with a larger sample so that the sample mean is likely to be closer to the population mean.

Many of the quantitative techniques fall into two broad categories: # Interval estimation # Hypothesis tests Interval Estimates It is common in statistics to estimate a parameter from a sample of data. The value of the parameter using all of the possible data, not just the sample data, is called the population parameter or true value of the parameter. An estimate of the true parameter value is made using the sample data. This is called a point estimate or a sample estimate. For example, the most commonly used measure of location is the mean. The population, or true, mean is the sum of all the members of the given population divided by the number of members in the population. As it is typically impractical to measure every member of the population, a random sample is drawn from the population. The sample mean is calculated by summing the values in the sample and dividing by the number of values in the sample. This sample mean is then used as the point estimate of the population mean. Interval estimates expand on point estimates by incorporating the uncertainty of the point estimate. In the example for the mean above, different samples from the same population will generate different values for the sample mean. An interval estimate quantifies this uncertainty in the sample estimate by computing lower and upper values of an interval which will, with a given level of confidence (i.e., probability), contain the population parameter. Hypothesis Tests Hypothesis tests also address the uncertainty of the sample estimate. However, instead of providing an interval, a hypothesis test attempts to refute a specific claim about a population parameter based on the sample data. For example, the hypothesis might be one of the following: * the population mean is equal to 10 * the population standard deviation is equal to 5 * the means from two populations are equal * the standard deviations from 5 populations are equal To reject a hypothesis is to conclude that it is false. However, to accept a hypothesis does not mean that it is true, only that we do not have evidence to believe otherwise. Thus hypothesis tests are usually stated in terms of both a condition that is doubted (null hypothesis) and a condition that is believed (alternative hypothesis). Website--http://www.itl.nist.gov/div898/handbook/eda/section3/eda35.htmP.s "Just giving info on what you don't know" - ;) Sillypinkjade----

The relations depend on what measures. The sample mean is an unbiased estimate for the population mean, with maximum likelihood. The sample maximum is a lower bound for the population maximum.

You can estimate them both.

If the sample consisted of n observations, then the degrees of freedom is (n-1).

The data point is close to the expected value.

875,953 people live in Delaware as of 2008.But, the 2010 estimate for it's population is 896,880 people so this would mean that right now, it's population is between these two populations.Sorry, I could not find the 2009 population on the internet.

be a wenis to find out

In statistics, point estimation is the process of providing a number or vector (which could be an infinite dimensional vector such as a function) that is stochastically 'close' in some sense to the actual value of that number or vector. For example, suppose that a population of people has a known mean height of 180 cm and an unknown standard deviation. Point estimation could be applied to a sample from this population to obtain an estimate of the standard deviation of its heights.

You use the sample mean as the estimate of the population mean; then as standard practice place confidence limits on the mean; most use 95%.

In Computing, Floating Point refers to a method of representing an estimate of a real number in a way which has the ability to support a large range of values.

The population mean is the mean value of the entire population. Contrast this with sample mean, which is the mean value of a sample of the population.

It means you can take a measure of the variance of the sample and expect that result to be consistent for the entire population, and the sample is a valid representation for/of the population and does not influence that measure of the population.

The derivative at any point in a curve is equal to the slope of the line tangent to the curve at that point. Doing it in terms of the actual expression of the curve, find the derivative of the curve, then plug the x-value of the point into the derivative to find the derivative at that point.

Demographics are the quantifiable statistics of a given population. Demographics are also used to identify the study of quantifiable subsets within a given population which characterize that population at a specific point in time.

In reality, a statistician never really has ALL the data. The data is instead taken from a sample of the whole population. If this sample is representative of the entire population, then any statistics based on the sample should be good estimates of the whole but probably not a perfect match. Of course the more data you get from the whole population the better the estimate, but it will always be an estimate unless you census the enitire population.