madscientiststats.blogspot.com
Mad Scientist (Statistics): F-test
http://madscientiststats.blogspot.com/2007/11/f-test.html
The F-test is a type of significance test. Which compares the standard deviations of two samples in order to determine if the standard deviations of their parent populations are equal. We give the standard deviations of the populations the symbols σ. We give the standard deviations of the samples the symbols s 1. The hypothesis in an F-test are:. The test statistic (F) is given by:. To find at what level your data is significant you can look at a table of F distribution critical values. ( some here.
madscientiststats.blogspot.com
Mad Scientist (Statistics): Robustness
http://madscientiststats.blogspot.com/2007/10/robustness.html
Is robust if it still produces roughly accurate results when the conditions for its use are not met. Procedures can have different degrees of robustness against different influences. Robustness can be influenced by a number of factors, e.g. the number of observations in the samples used for the procedures. Posted by Chris @ 12:01 PM. Tuesday, October 23, 2007.
madscientiststats.blogspot.com
Mad Scientist (Statistics): Power of a Test
http://madscientiststats.blogspot.com/2007/11/pwer-of-test.html
Power of a Test. The power of a significance test. Against a particular H a. Is the probability of it rejecting H 0. When that H a. Is true. To find this probability we do the following: (for a one-sample t). Using the normal steps of a significance test find out which values of t will lead us to reject H 0. Eg reject H 0. Rearrange the one-sample test statistic to work out what values x. Needs to take for us to reject H 0. Eg if t 3 then:. Find the probability of x. Taking such values if H a.
madscientiststats.blogspot.com
Mad Scientist (Statistics): Statistical Inference
http://madscientiststats.blogspot.com/2007/10/statistical-inference.html
Statistical inference is drawing conclusions about populations. Using data collected from samples of those populations. Some important statistical inference methods are:. Confidence intervals are a type of statistical inference where we create an interval using data from a sample and estimate the probability that some population parameter is found within (captured by) that interval. Intervals are often described in the form "the interval a b" (which means the interval from a - b to a b.).
madscientiststats.blogspot.com
Mad Scientist (Statistics): November 2007
http://madscientiststats.blogspot.com/2007_11_01_archive.html
The F-test is a type of significance test. Which compares the standard deviations of two samples in order to determine if the standard deviations of their parent populations are equal. We give the standard deviations of the populations the symbols σ. We give the standard deviations of the samples the symbols s 1. The hypothesis in an F-test are:. The test statistic (F) is given by:. To find at what level your data is significant you can look at a table of F distribution critical values. ( some here.
madscientiststats.blogspot.com
Mad Scientist (Statistics): October 2007
http://madscientiststats.blogspot.com/2007_10_01_archive.html
Confidence Intervals for Population Means. Confidence intervals are a type of statistical inference. Where we create an interval using data from a sample and estimate the probability that some population parameter is found within (captured by) that interval. Intervals are often described in the form "the interval a b" (which means the interval from a - b to a b.). A level C confidence interval. For a parameter is an interval which has a C percent chance of capturing that parameter. Of its mean ( µ).
madscientiststats.blogspot.com
Mad Scientist (Statistics): Matched Pairs Inference
http://madscientiststats.blogspot.com/2007/11/matched-pairs-inference.html
One-sample t-procedures can be used for inference on matched pairs experiments. The one-sample t-procedures are used on the differences between the subjects of the pairs. This effectively turns the matched pairs data into a single sample. Two-sample t-procedures cannot be used for inference on matched pairs experiments because the samples are not independent. Posted by Chris @ 8:58 PM. Friday, November 23, 2007.
madscientiststats.blogspot.com
Mad Scientist (Statistics): September 2007
http://madscientiststats.blogspot.com/2007_09_01_archive.html
Graphs/Charts for Qualitative Variables. Always label all axis/categories etc, include a title, and give units if necessary.). A description of the distribution of a qualitative variable. Gives the categories and the number/% of individuals in those categories. Two common graphical methods of displaying the distribution of a qualitative variable are:. Give the categories and show the % of individuals in those categories as a fraction of the circle - as in the diagram below. Posted by Chris @ 5:46 AM.
madscientiststats.blogspot.com
Mad Scientist (Statistics): Two-sample t-procedures: Comparing Means
http://madscientiststats.blogspot.com/2007/11/two-sample-t-procedures-comparing-means.html
Two-sample t-procedures: Comparing Means. We can use statistical inference. To draw conclusions about a population by looking at a sample of that population. In that same way, we can use statistical inference to draw conclusions about the difference between populations. By looking at the difference between samples of those populations. If the means of our two populations are µ. Then the difference between them is(obviously) µ. We can make confidence intervals. And perform significance tests. The degrees ...
