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A sample mean is the best point estimate of the?
he population mean
What is point estimation?
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.
Do small samples and large population variances usually underestimate or be close to population mean?
Small samples and large population variances imply that the estimate for the mean will be relatively poor. Whether or not it will result in an underestimate or overestimate depends on the distribution: with a symmetric distribution the two outcomes are equally likely.
N equals 36 with a population mean of 74 and a mean score of 79.4 with a standard deviation of 18?
Can someone help me find the answer for a sample n=36 with a population mean of of 76 and a mean of 79.4 with a standard deviation of 18?
How do you find the raw score when you don't have the z score?
Go back to the basic data, estimate the sample mean and the standard error and use these to estimate the Z-score.
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.
True or false the sample mean is a point of estimate of the population mean?
Nearly true. It is a point estimate, not point ofestimate.
Is the point estimate the same as the average?
No. The average of a dataset is the point estimate for the mean of the population.
A sample mean is the best point estimate of the?
he population mean
How do you compute the point estimate of a population mean?
To compute the point estimate of a population mean, you take the sample mean. This is done by calculating the average of the data values in the sample. The sample mean is then used as an estimate of the population mean.
What is the best estimator of population mean?
The best estimator of the population mean is the sample mean. It is unbiased and efficient, making it a reliable estimator when looking to estimate the population mean from a sample.
Number used to estimate a population parameter?
A point estimate is a single value used to estimate a population parameter, such as the sample mean used to estimate the population mean. Confidence intervals can also be used to provide a range within which the population parameter is likely to lie.
What is the difference between the population mean and sample mean?
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.
What is the difference between calculating the sample mean and the population 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.
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--http://www.itl.nist.gov/div898/handbook/eda/section3/eda35.htmP.s "Just giving info on what you don't know" - ;) Sillypinkjade----
TO ESTIMATE THE AVERAGE AGE OF STUDENTS A SAMPLE OF 50 PARTICIPANTS WAS OBTAINED, WITH A MEAN OF 16 AND A POPULATIONAL VARIATION OF 9, calculate:* POINT ESTIMATE OF THE MEANS* 95% AND 99% CONFIDENCE INTERVAL ESTIMATES FOR THE MEANS?
Point Estimate of the Mean: The point estimate of the mean is 16, since this is the sample mean. 95% Confidence Interval Estimate for the Mean: The 95% confidence interval estimate for the mean can be calculated using the following formula: Mean +/- Margin of Error = (16 +/- 1.96*(9/sqrt(50))) = 16 +/- 1.51 = 14.49 to 17.51 99% Confidence Interval Estimate for the Mean: The 99% confidence interval estimate for the mean can be calculated using the following formula: Mean +/- Margin of Error = (16 +/- 2.58*(9/sqrt(50))) = 16 +/- 2.13 = 13.87 to 18.13
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.
