cp /afs/acpub/project/sta215/ex02_10.dat .To see the data set type
less ex02_10.dat
ALternatively you can download the data files using
your web browser from
here .
Use the MINITAB commands set or read to import the data set into MINITAB. See the page Intro to Minitab.
The variables are: (1) homeless: homeless/1000 pop obtained from HUD 1984 report on homeless. Tucker adjusted Cleveland, Balto and NY upward. (2) numhless: number of homeless people (3) poverty: percent below poverty line based on 1979 census (4) unemploy: 1984 unemployement rates (5) publich: public housing/1000 (6) pop: pop in 1000 (7) meantemp: mean temperature (8) vacancy: vacancy rate (1984) (9) rentctl: =1 for rent control (note that Worcester MA has a grow limiting "movement" according to Tucker but has no formal rent control ordinance)Hint: To read in the data set use read 'homeless.data' c1-c9 Minitab will then automatically ignore the non-numeric 10th column with the city name.
Hint: To read the data into minitab use read 'state.data' c1-c8. Minitab will automatically ignore the 9-th non-numeric column.
A previous study in Africa had suggested that migration from a primitive society to a modern one might increase blood pressure at first, but that the blood pressure would tend to decrease back to normal over time.
The anthropologists also measured the height, weight, and a number of other characteristics of the subjects. A portion of their data is given below. All these data are for males over 21 who were born at a high altitude and whose parents were born at a high altitude. The skin-fold measurements were taken as a general measure of obesity. Systolic and diastolic blood pressure usually are studied separately. Systolic is often a more sensitive indicator.
The variables are: (1) age: age in years (2) years: years since migration (3) weight: weight in kilograms (4) height: height in millimeters (5) chin: chin skin fold in millimeters (6) forearm: forearm skin fold in millimeters (7) calf: calf skin fold in millimeters (8) pulse: pulse rate in beats per minute (9) systol: systolic blood pressure (10) diastol: diastolic blood pressure
The data give statistics for automobiles of the 1979 model year as sold in the United States.
Hint: To read in the data set use read 'auto.data' c1-c12. Minitab will then automatically ignore the non-numeric 13th column with the car model name.
Both overall weather conditions and the size of a house can greatly affect energy consumption. A simple formula was used to try to adjust for this. Average energy consumed by the house during one period was recorded as (consumption)/[(weather)(house area)], where consumption is total energy consumption for the period, measured in BTU's, weather is measured in number of degree days, and house area is measured in square feet. In addition, various characteristics of the house, chimney, and furnace were recorded for each house. A few observations were missing and recorded as *, Minitab's missing data code.
The variables are:
(1) type type of furnace: 1 = forced air,
2 = gravity, 3 = forced water
(2) ch.area chimney area
(3) ch.shape chimney shape: 1 = round, 2 = square, 3 = rectangular
(4) ch.ht chimney height (in feet)
(5) ch.line type of chimney liner: 0 = unlinded, 1 = tile, 2 = metal
(6) house type of house: 1 = ranch, 2 = two-story, 3 = tri-level,
4 = bi-level, 5 = one and a half stories
(7) age house age in years (99 means 99 or more years)
(8) btu.in average energy consumption with vent damper in
(9) btu.out average energy consumption with vent damper out
(10) damper type of damper: 1 = EVD, 2 = TVD
The variables are: (1) pulse1 -- first pulse rate (2) pulse2 -- second pulse rate (3) ran -- 1 = ran in place, 2 = did not run in place (4) smokes -- 1 = smokes regularly, 2 = does not smoke regularly (5) sex -- 1 = male, 2 = female (6) height -- height in inches (7) weight -- weight in pounds (8) activity -- usual level of physical activity: 1 = slight, 2 = moderate, 3 = a lot
Nineteen of Wisconsin's counties were selected for the study. Lists of restaurants were drawn up from telephone directories and these were sampled in porportion to the population of the county. A sample of 1000 restaurants yielded 279 usable responses. The data set thus consists of 279 cases, one for each restaurant in the usable data.
The variables are:
(1) id -- identification number
forc outlook -- values 1, 2, 3, 4, 5, 6, 7, denoting from very unfavorable
(2) -- to very favorable
(3) sales -- gross 1979 sales in \$1000's
(4) newcap -- new capital invested in 1979, in \$1000's
(5) value -- estimated market value of the business, in \$1000's
(6) costgood -- cost of goods sold as a percentage of sales
wages -- wages as a percentage of sales
(7) ads -- advertising as a percentage of sales
(8) typefood -- 1 = fast food, 2 = supper club, 3 = other
(9) seats -- number of seats in dining area
(10) owner -- 1 = sole proprietorship, 2 = partnership, 3 = corporation
(12) ft.empl -- number of full-time employees
(13) pt.empl -- number of part-time employees
(14) size -- size of restaurant:
1 = 1 to 9.5 full-time equivalent employees,
2 = 10 to 20 full-time equivalent employees.
(part-time employees are each counted as 1/2 of a
full-time employee).
There are 9 columns corresponding to the variables:
AGE (in years); HEIGHT (in inches); WEIGHT (in pound); RELIGION (1=Catholic, 2=Protestant, 3=Jewish, 4=Other, 5=None); MAJOR (1=psych, 3=bio, 4=pps, 5=soc, 11=other); attitude about abortion legislation: ABVIEW (1= ``unrestricted pro choice'' to 4=''unrestricted pro life''); political leaning: POL (1=very conservative to 5=very liberal), attitude towards STA 110 before the first class: BEFORE (0=very negative to 8=very positive); attitude after first class: AFTER (0 to 8).