This java applet can be used to determine whether or not the means in two sample populations are significantly different.
This java applet can be used to determine whether or not the means in two sample populations are significantly different.
This simulation involves a series of balls passing through bins to eventually yield a normal distribution. Information is also provided about what the normal distribution is.
A searchable database of approximately 600 applets for teaching introductory statistics topics, including graphical displays, descriptive statistics, probability concepts, random variables, sampling and sampling distributions, confidence intervals, hypothesis testing, ANOVA, chi-square tests, correlation and regression, time series and forecasting, decision analysis, and quality control charts. Applets are arranged by topic and intended use. Information on each applet includes source and url as well as a brief description.
DataFerrett is a unique data analysis and extraction tool -- with recoding capabilities -- to customize federal, state, and local data to suit your requirements. Using DataFerrett, you can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands. The DataFerrett helps you locate and retrieve the data you need across the Internet to your desktop or system, regardless of where the data resides. You can then develop and customize tables. Selecting your results in your table you can create a chart or graph for a visual presentation into an html page. Save your data in the databasket and save your table for continued reuse. The DataFerrett is a Beta testing version that will incorporate the latest bug fixes, enhancements, and new functionality that will be rolled into the DataFerrett after testing has been completed.
This applet demonstrates the concept of power. Users select the hypothesized mean, the alternative mean, the sample size, and the number of samples. The applet shows the hypothesized histogram and the alternative histogram. Users then select either the level of significance and set alpha or the rejection region and set the test statistic. The applet then shows the p-value (in red) and power (in green). User can also determine the direction of the test by clicking the inequality sign.
This page of Statistical Java describes 11 different probability distributions including the Binomial, Poisson, Negative Binomial, Geometric, T, Chi-squared, Gamma, Weibull, Log-Normal, Beta, and F. Each distribution has its own applet in which users can manipulate the parameters to see how the distribution changes. The parameters are described on the main page as well as situations that would use each distribution. The equations of the distributions are not given. To select between the different applets you can click on Statistical Theory, Probability Distributions and then the Main Page. At the bottom of this page you can make your applet selection. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/
(Uses JAVA) Some basic statistical analysis tools that allow the user to input their own data or use the pre-existing data and perform the desired test (e.g ANOVA, Descriptive, t-test, chi-square, correlation and regression).
Use the Sample Size Calculator to determine the sample size you need in order to get results that reflect the target population as precisely as needed. You can also find the level of precision you have in an existing sample. The site also describes terms you need to know to understand confidence intervals and what they mean.
This website is a collection of analysis tools commonly used in statistics and mathematics. These tools are divided into 7 categories: 1) Summarizing Data 2)Computational Probability 3)Requirements for most tests and computations 4) One population and one variable 5)One population and two or more variables 6)Two or three populations and one variable 7) Several populations and one or more variables
A computational tool that runs the one-way ANOVA by the user inputing individual data or by copying and pasting a delimitted data set.