The worksheet contains data on 89 different kinds of cars. From Appendix F (page F-2) of Levine, Berenson & Stephan, we see that the data set contains make & model, drive type (0=rear, 1=front), MPG, fuel type (0=premium, 1=regular), fuel tank capacity (in gallons), length of the car (in inches), wheelbase (inches), width (inches), turning circle (feet), weight (lbs), luggage capacity (cubic feet), front leg room (inches), and front head room (inches).
make & model, drive type (0=rear, 1=front), MPG, fuel type (0=premium, 1=regular), fuel tank capacity (in gallons), length of the car (in inches), wheelbase (inches), width (inches), turning circle (feet), weight (lbs), luggage capacity (cubic feet), front leg room (inches), and front head room (inches)
The "Beer" data set contains data on 69 different kinds of beer, including brand, price (in dollars), calories, alcohol content (%), type (craft lager=1, craft ale=2, imported lager=3, regular and ice beer=4, light and no alcohol beer=5), and country of origin (US=1, imported=0).
Please see the Dataset Description for variable descriptions.
The Emeadow file contains data on 74 different kinds of houses. It contains 14 variables: Value (in dollars), Lotsize (in acres), Bed (number of), Bath (number of), Rooms (number of), Age (in years), Taxes (in dollars), Eat in kitchen (0 = no, 1=yes), CAC (0=no, 1=yes), Fireplace (0=no, 1=yes), Sewer (0=no, 1=yes), Basement (0=no, 1=yes), Modern kitchen (0=no, 1=yes), and Modern bathrooms (0=no, 1=yes).
The "farming" data set contains information about 60 home sales. Variables include value of the home, number of rooms and other features, as well as sewer connection and central air-conditioning.
Name Type Description Value Continuous Assessed value of home in thousands of dollars Lotsize Continuous In acres Bed Descrete Number of bedrooms Bath Continuous Number of bathrooms Rooms Descrete Number of rooms Age Continuous In years Taxes Continuous Taxes paid per year Locate Indicator Location (1-3) Eat-in-kit Indicator Eat in kitchen (1=yes, 0=no) CAC Indicator Central air-conditioning (1=yes, 0=no) Fireplace Indicator Fireplace (1=yes, 0=no) Sewer Indicator Connected to local Sewer system (1=yes, 0=no) Basement Indicator Basement (1=yes, 0=no), Modkit Indicator Modern kitchen (1=yes, 0=no), Modbath Indicator Modern bathroom (1=yes, 0=no)
The "international" data set contains information on 46 country. Variables include gross domestic product, total area, and other features.
Name Type Description Co Discrete Country name GDP Continuous Gross Domestic Product per capita in thousands Area Continuous Total Area (square kilometers) G20 Discrete Member of the G-20 group of industrial nations to promote international financial stability (0=nonmember, 1=member) Petro Discrete Country has petroleum as a natural resource (0=np, 1=petroleum is a natrual resource, 2=country is a member of OPEC (Organization of Petroleum Exporting Countries Pop Discrete Population (expressed in thousands) >65 Continuous Percent of Population aged 65 years and over LifExp Continous Life expectancy at birth Lit Continous Literacy: percent of population age 15 or more that can read and write Labor Discrete Labor force (expressed in millions) Unemp Continous Percent unemployment Exp Continous Exports expressed in billions of dollars Imp Continous Imports expressed in billions of dollars Cellfone Discrete Number of mobile or cellular phones expressed in millions
The "realestate" data set contains information on 103 home sales in Colorado in 2003. Variables include selling price of the home, size of the home and other features, as well as distance from the center of the city and township.
Name Type Description Price Continuous Selling price of the home in thousands of dollars Bedrooms Discrete Number of bedrooms Size Continuous Size of the home in square feet Pool Discrete Pool (1 = yes, 0 = no) Distance Continuous Distance from the center of the city Twnship Indicator Township (1-5) Garage Indicator Garage (1 = yes, 0 = no) Baths Continuous Number of bathrooms
This collection of data represents demographic data from workers from different industries.
Name Type Description Salary Continuous Total yearly income Industry Categorical (Manf = manufacturing, Const = Construction) Occuption Categorical (mana = management, sale = sales, cler = clerical, serv = service) Education Continuous Years of Education Nonwhite Categorical (yes or no) Hispanic Categorical (yes or no) Gender Categorical (Female or Male) Experience Continuous Years of work Experience Married Categorical (yes or no) Age Continuous Age in Years Union Categorical (yes or no)
There is an assumption that salaries will increase as workers gain more experience and therefore, become more productive and more valuable to their employers. Inflation also increases salaries. By investigating a sample of workers at a given time in their careers, we can examine if there is a relationship between salary and length of employment.
Name Type DescriptionSalary Continuous Total yearly income (in thousands of dollars) LOE Continuous Length of employment (in months) Div Discrete Research or Manufacturing
The "seventhgrade" data set contains data on 78 different characteristics of seventh graders, including ID, GPA, IQ, Gender, and Self Concept.
Please see the Dataset Description for variable descriptions.
The "univCol" data set contains information on 50 colleges and universities.
Variable Name Type of Variable Description Name Categorical school name Term Discrete type of term (semester=1, other=0) Location Discrete location (urban=1, suburban=2, rural=3) School Discrete type of school (public=0, private=1) SAT Continuous average total SAT score TOEFL Discrete TOEFL score (<550 = 0, >= 550 = 1) Room Continuous room and board expenses (in thousands of dollars) Cost Continuous annual total cost (in thousands of dollars) Indebtedness Continuous average indebtedness at graduation (in thousands of dollars)
The "wages2003" data set contains information on annual wages for 100 workers. It includes variables relating to industry, years of education, and gender for each worker. Additional descriptive variables include occupation, work experience, union status, and additional demographic information.
Name Type DescriptionWage Continuous Annual wage in dollars Occupation Discrete 1 = Mgmt, 2 = Sales, 3 = Clerical, 4 = Services, 5 = Prof, 0 = Other Ed Discrete Years of education South Factor Southern resident? (1 = Yes, 0 = No) Nonwh Factor Non-white? (1 = Yes, 0 = No) Hisp Factor Hispanic? (1 = Yes, 0 = No) Fe Factor Female? (1 = Yes, 0 = No) Marr Factor Married? (1 = Yes, 0 = No) Age Discrete Age in years Union Factor Union member? (1 = Yes, 0 = No)