This activity uses Microsoft Excel to estimate the population variance of grouped data two ways: the variance within a group and the variance between groups. This activity accompanies Section 7.3 of Data Matters.
This topic from an online textbook discusses standard error, confidence interval, and significance testing for a difference in percentages or proportions. It also covers paired alternatives and standard error of a total. Exercises and answers are also provided.
This section of an online textbook discusses calculating the exact probability using observed sets of frequencies, constructing frequency tables, and computing p-values. Exercises and answers are provided.
This section of an online textbook discusses the correlation coefficient and illustrated it visually through graphs. It explains calculations as well as how scatter plots can describe data. It covers significance tests for relationships, the Spearman rank correlation and the regression equation. Exercises and answers are included.
Survival analysis is concerned with studying the time between entry to a study and a subsequent event. This site looks at the Kaplan-Meier survival curve, its method and how to calculate it. It provides exercises as well as answers.
This article presents a dataset containing actual monthly data on computer usage in Best Buy stores from August 1996 to July 2000. This dataset can be used to illustrate time-series forecasting, causal forecasting, simple linear regression, unequal error variances, and variable transformation. Key Words: Model-building; Seasonal Variation.
The dataset presented in this article comes from a South African study of growth of children. This data is a useful example of Simpson's paradox. Key Words: Categorical data; Comparing proportions.
This article describes a dataset containing information for 25 brands of domestic cigarettes. The dataset can be used to illustrate multiple regression, outliers, and collinearity.
This article describes a dataset containing monthly household electric billing charges for ten years. The data can be used to illustrate graphing, descriptive statistics, correlation, seasonal decomposition, a variety of smoothing methods, ARIMA models, forecasting, and multiple regression.
The dataset presented in this article contains body measurements for 252 men and can be used to illustrate multiple regression and to provide practice in model building.
