auto96

Dataset Description:

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).

Variable Descriptions:

 
 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)
 

beer

Dataset Description:

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).

Variable Descriptions:

 
 Please see the Dataset Description for variable descriptions.

csdata

CS DATA

Data for Spring 2005

emeadow

Dataset Description:

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).


Farming

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.

Variable Descriptions:


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)

intldata

Dataset Description:

The "international" data set contains information on 46 country.  Variables include gross domestic product, total area, and other features.

Variable Descriptions:


 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
 

Realestate data

Dataset Description:

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.

Variable Descriptions:


 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
 

Salaries

Dataset Description:

This collection of data represents demographic data from workers from different industries.

Variable Descriptions:


 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)
 

Salary Data

Dataset Description:

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.

Variable Descriptions:

 Name          Type              Description
 Salary        Continuous        Total yearly income (in thousands of dollars)
 LOE           Continuous        Length of employment (in months)
 Div           Discrete          Research or Manufacturing
 

seventh grade

Dataset Description:

The "seventhgrade" data set contains data on 78 different characteristics of seventh graders, including ID, GPA, IQ, Gender, and Self Concept.

Variable Descriptions:

 
 Please see the Dataset Description for variable descriptions.

University and College Data

Dataset Description:

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)  

wages2003

Dataset Description:

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.

Variable Descriptions:

 Name          Type              Description
 Wage          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)