This article provides a data collection and analysis activity for illustrating simple linear regression and outlier analysis. The activity was designed to involve students in the process of data collection and to motivate studying the relationship between two quantitative variables. Students collect data on occurrences of letters in English text. These data are used to study the relationships between how often a letter occurs in English text, and: (1) the letter's Morse Code units and (2) the relative frequency of Scrabbleä‹¢ game tiles for the letter. Worksheets and answers to the activities are provided.
This Department of Energy website provides weekly average gasoline prices for several regions, states and cities. The averages are produced from a weekly survey of around 800 retail gasoline stations. The site includes information on data collection methods, survey methodology and historical data.
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.