This webpage provides instructions for teaching sampling distributions using Sampling SIM software. It includes information regarding prerequisite knowledge, common misconceptions, and objectives, as well as links to an activity and a pre/post-test.
This webpage provides instructions for teaching p-values and standard distributions using Sampling SIM software. It includes information regarding prerequisite knowledge, common misconceptions, and objectives, as well as links to an activity and a pre/post-test.
This webpage provides instructions for teaching confidence intervals using Sampling SIM software. It includes information regarding prerequisite knowledge, common misconceptions, and objectives, as well as links to an activity and a pre/post-test.
Stattucino is a free Java-based system for data analysis. This service is available as a Java applet or application. Some statistics are provided by a web-based interface as servlets. The applet and the application have a spreadsheet type interface for entering data, whereas the servlets use a html form for entering data. The output produced by the servlets, the applet and the application are in html.
The goal of this assignment is to obtain summary statistics for the variables in the data set, ncbirth1450.xls, which represents a random sample of 1450 births from the state of North Carolina.
This assignment has students investigate whether the risk of having a child with a low birth weight is higher when the mother drinks and smokes during pregnancy. The data set represents a random sample of 1450 births from the state of North Carolina.
The assignment begins with creating a summary of and tables for the data, then walks the student through the steps of creating a hypothesis testing report. It uses the data set ncbirth200.xls, which is a random sample of 200 births from the data set ncbirth1450.xls.
This outline, appropriate for an introductory statistics course, describes steps in a statistical study including identifying the question, designing a study, collecting data, analyzing data and making conclusions.
This online textbook provides information on the statistical analysis of nutritional data. Techniques covered include data cleaning, descriptive statistics, histograms, graphics, scatterplots, outlier identification, regression and correlation, confounding, and interactions. Each chapter includes exercises with real data and self-tests to be used with SPSS.
