This article describes a dataset containing information on 308 diamond stones, which is useful when studying concepts in multiple linear regression analysis. Key Words: Categorical variables; Data transformation; Standardized residuals.
This dataset contains the time of birth, sex, and birth weight for 44 babies born in one 24-hour period at a hospital in Brisbane, Australia. The data can be used for studying some common distributions like the normal, binomial, geometric, Poisson, and exponential.
The data presented in this article refer to the reliability of ball bearings in manufacturing. Rather than exploring the data to obtain a multiple linear regression solution, a theoretically derived equation is given and the data is used to test it. Key Words: Failure times; Percentiles; Weighted least squares.
The dataset presented in this article provides the salary and performance data for non-pitchers for the 1992 Major League Baseball season. Exploratory data analysis is used to determine a suitable regression model for the data. Key Words: Model selection and validation; Stepwise model selection.
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 presents a dataset containing the 1970 draft lottery information, which illustrates a nonrandom procedure. Key Words: Chi-square; Correlation; Exploratory data analysis; Graphical analysis; Randomness; Regression.
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.
