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What would happen to the sampling distribution of the mean if you increased the sample size from 5 to 25?
The variance of the estimate for the mean would be reduced.
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.
Advantages and disadvantages of using arithmetic mean?
"The advantage is that the mean takes every value into account. A disadvantage is that it can be affected by extreme values. " The mean or more properly the "arithmetic mean" of a sample will eventually approximate the mean of the distribution of the population as the sample size increases. If the population distribution is skewed (not symmetrical), the mode and median will not provide an estimate of the mean, even as the sample size becomes large.
What is the difference between statistics and parameters?
Parameter is any attribute Statistic are the measured values of a parameter. A statistic is a sample value such as the average height of a group of students. A parameter is a functional constant such as the mean of a normal distribution. Statistics are often used to estimate parameters. For instance, a sample average is an estimate of the mean.
Why wont a sample statistics change from sample to sample?
The sample mean may differ from the population mean, especially for small samples.
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 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.
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.
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.
A survey of 35 students of a university showed a sample mean age of 21.7 with a standard deviation of 3.1 years What is the point estimate of the students' mean age?
It is the sample mean age of 21.7.
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
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 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.
When using the distribution of sample mean to estimate the population mean what is the benefit of using larger sample sizes?
The variance decreases with a larger sample so that the sample mean is likely to be closer to the population mean.
How does one calculate the standard error of the sample mean?
Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.
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 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----
