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point estimate

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โˆ™ 2012-04-13 17:05:56
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Q: What is used to estimate the population parameter?
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What sample statistic is used to estimate a population parameter?

A point estimate is a single value (statistic) used to estimate a population value (parameter)true apex

Is point estimate a range of values used to estimate a population parameter?

mabye, mabye not

Number used to estimate a population parameter?

The confidence level is 90% the sample size is n=112 and -o =17.

What is the relationship between sample statistic and population parameter?

A sample statistic uses a smaller group, or sample, from the larger population. In this manner, a sample statistic seeks to estimate a population parameter.

If a population consisted of 75000 people surveying 75000 people from the population could result in a parameter but not a point estimate.?


Is the sample mean a point estimate of the population mean?

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.

What is the absolute value of the difference between the point estimate and the population parameter it estimates?

B. The sampling error

Does a parameter ever change for a set population?

No, a parameter never changes for a set population.

Does a parameter describe a population or a sample?

A parameter describes a population. A statistic describes a sample.

What is Hypothesis Testing of Point Estimate?

A statistical estimate of the population parameter.

A parameter is a numerical descriptor that is used to describe a population or a sample?


What is the single parameter in the binomial distribution and what sample statistic would be used to estimate it?

The binomial distribution is defined by two parameters so there is not THE SINGLE parameter.

What is the difference between a parameter and a statistic?

A parameter is a number describing something about a whole population. eg population mean or mode. A statistic is something that describes a sample (eg sample mean)and is used as an estimator for a population parameter. (because samples should represent populations!)

Is a single number characteristic describe a population is called a parameter?


Does a random largely selected sample always give a better estimate of the population than a randomly selected sample?

A larger random sample will always give a better estimate of a population parameter than a smaller random sample.

Does the confidence interval always contain the true population parameter?

No, the confidence interval (CI) doesn't always contain the true population parameter. A 95% CI means that there is a 95% probability that the population parameter falls within the specified CI.

A numerical summary of a population is a?


What is prior parameter v.s. post hoc parameter?

A priori simply means "prior to", so it is something done prior to conducting your experiment. Post hoc means "after", so doing it after the study is complete. A parameter is a number associated with the the population, so an a priori parameter could be the mean of the population prior to an event such as your experiment and a post hoc parameter would then be the population mean after the event.

How does a parameter and a statistic differ?

A parameter is a numerical measurement of a population; a statistic is a numerical measurement of a sample.

What are quantitative techniques?

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-- "Just giving info on what you don't know" - ;) Sillypinkjade----

Differentiate estimate and estimator?

It can get a bit confusing! The estimate is the value obtained from a sample. The estimator, as used in statistics, is the method used. There's one more, the estimand, which is the population parameter. If we have an unbiased estimator, then after sampling many times, or with a large sample, we should have an estimate which is close to the estimand. I will give you an example. I have a sample of 5 numbers and I take the average. The estimator is taking the average of the sample. It is the estimator of the mean of the population. The average = 4 (for example), this is my estmate.

The characteristic or value of a population that is under consideration is called a population?


Why and when should programmers use a constant reference parameter?

when we use that parameter as a global parameter and we used that parameter through out the program without changing

What is the the relationship between population and sample parameter and statistic?

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.

What are the main qualities of a good estimator?

A "Good" estimator is the one which provides an estimate with the following qualities:Unbiasedness: An estimate is said to be an unbiased estimate of a given parameter when the expected value of that estimator can be shown to be equal to the parameter being estimated. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn. Unbiasedness is a good quality for an estimate, since, in such a case, using weighted average of several estimates provides a better estimate than each one of those estimates. Therefore, unbiasedness allows us to upgrade our estimates. For example, if your estimates of the population mean µ are say, 10, and 11.2 from two independent samples of sizes 20, and 30 respectively, then a better estimate of the population mean µ based on both samples is [20 (10) + 30 (11.2)] (20 + 30) = 10.75.Consistency: The standard deviation of an estimate is called the standard error of that estimate. The larger the standard error the more error in your estimate. The standard deviation of an estimate is a commonly used index of the error entailed in estimating a population parameter based on the information in a random sample of size n from the entire population.An estimator is said to be "consistent" if increasing the sample size produces an estimate with smaller standard error. Therefore, your estimate is "consistent" with the sample size. That is, spending more money to obtain a larger sample produces a better estimate.Efficiency: An efficient estimate is one which has the smallest standard error among all unbiased estimators.The "best" estimator is the one which is the closest to the population parameter being estimated.

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