This website is provides an online text version of Grinstead & Snell's "Introduction to Probability" as well as supplemental reference information.
This website is provides an online text version of Grinstead & Snell's "Introduction to Probability" as well as supplemental reference information.
This software allows you to extract data from published graphs. There is a web-based app and a downloadable version. First, you provide the software with a picture of the graph in question. Then you give it two points on the x-axis and two points on the y-axis for reference. Then you click on the points on the graph that you want to extract. The points are put into a .csv file.
This software makes it easier to use the R language. It includes a code debugger, editing, and visualization tools.
This online software allows you to load data and make professional-looking graphs with it. Graph types are basic (scatterplot, line plot, bar charts, etc.), statistical (histograms, box plots), scientific (error bars, heat map, contour), 3D charts, and financial (e.g. time series). Other graphs are available with the paid pro version. Log in is required, which allows you to upload data and save it for next use.
Rseek.org is a search engine for R resources. Type any topic in the search box, and get resources that are R specific. You can further narrow your search to just articles, books, packages, support, or "for beginners."
Calculate the number of respondents needed in a survey using our free sample size calculator. Our calculator shows you the amount of respondents you need to get statistically significant results for a specific population. Discover how many people you need to send a survey invitation to obtain your required sample. You can also calculate the margin of error based on your sample size.
This is an online calculator that can be used to determine the recommended sample size that is needed for a specific margin of error, confidence level, and population size.
March 24, 2009 Activity webinar presented by Nicholas Horton, Smith College, and hosted by Leigh Slauson, Otterbein College. Students have a hard time making the connection between variance and risk. To convey the connection, Foster and Stine (Being Warren Buffett: A Classroom Simulation of Risk and Wealth when Investing in the Stock Market; The American Statistician, 2006, 60:53-60) developed a classroom simulation. In the simulation, groups of students roll three colored dice that determine the success of three "investments". The simulated investments behave quite differently. The value of one remains almost constant, another drifts slowly upward, and the third climbs to extremes or plummets. As the simulation proceeds, some groups have great success with this last investment--they become the "Warren Buffetts" of the class. For most groups, however, this last investment leads to ruin because of variance in its returns. The marked difference in outcomes shows students how hard it is to separate luck from skill. The simulation also demonstrates how portfolios, weighted combinations of investments, reduce the variance. In the simulation, a mixture of two poor investments is surprisingly good. In this webinar, the activity is demonstrated along with a discussion of goals, context, background materials, class handouts, and references (extra materials available for download free of charge)
When performing a hypothesis test about the population mean, a possible reason for the failure of rejection of the null hypothesis is that there's an insufficient sample size to achieve a powerful test. Using a small data set, Minitab is used to check for normality of the data, to perform a 1-Sample t test, and to compute Power and Sample Size for 1-Sample t.
Big data analysis is explained in this online course that introduces the user to the tools Hadoop and Mapreduce. These tools allow for the parallel computing necessary to analyze large amounts of data.