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Data Set/Description Owner Last edited Size Views
2015-2016 NBA Salarieslauren.bartschApr 26, 201622KB156
Demographic Data - Multiple Geographical Areaslauren.bartschApr 12, 201612KB10
Stop And Frisk Data For January 2012
his data set contains information on the 69,073 stops made under the Stop And Frisk policy of the New York City police department in January of 2012. For the detainee, the variables include Sex (0 - female, 1- male), Race (1 - black, 2- black Hispanic, 3- white Hispanic, 4- white, 5 - Asian/Pacific Islander, 6 - American Indian), Age, Height and Weight. Note that Age, Height and Weight may be subject to coding errors based on some of the more extreme values. Other variables in the data set are FriskOrSearch (0 - if the stop did not result in either frisk or search, 1- otherwise), FoundSomething (0 - if the detainee was not found to have either contraband or weapons, 1 - otherwise) and ArrestMade (0 - if no arrest was made, 1 - otherwise).
lauren.bartschMar 15, 20162MB233
Happiness GSS Data Table from Book Examplelauren.bartschMar 1, 2016323B71
Happiness Data from GSS.xls
These data come from the 2008 General Social Survey. A subset of 190 respondents were selected at random from the full data set. Children = number of children. Education is highest year of education (e.g., 12 = High School; 16 = Bachelors, etc.). Happy: 1 = Not too happy, 2 = Pretty Happy, 3 = Very Happy. Health: 1 = Poor, 2 = Fair, 3 = Good, 4 = Excellent. Income: 1 = Under $1000; 2 = $1000-2999; 3 = $3000-3999; 4 = $4000-4999; 5 = $5000-5999; 6 = $6000-6999; 7 = $7000-7999; 8 = $8000-9999; 9 = $10000-12499; 10 = $12500-14999; 11 = $15000-17499; 12 = $17500-19999; 13 = $20000-22499; 14 = $22500-24999; 15 = $25000-29999; 16 = $30000-34999; 17 = $35000-39999; 18 = $40000-49999; 19 = $50000-59999; 20 = $60000-74999; 21 = $75000-$89999; 22 = $90000-$109999; 23 = $110000-$129999; 24 = $130000-$149999; 25 = $150000+. Married: 0 = No, 1 = Yes. Religious: 1 = Not religious, 2 = Slightly religious, 3 = Moderately religious, 4 = Very religious.
lauren.bartschFeb 28, 201612KB324
EXAM SCORESlauren.bartschFeb 16, 2016293B20
Countries Homicides.xlsx
Steven Broad (, a mathematics faculty member at Saint Mary’s College in Notre Dame, IN, collected this data in 2012 after the mass shooting in Aurora, CO, to help inform discussions about gun control. He used the web site, which contains data about homicides from a number of countries, as his source of information to compile this data set for 38 countries. That web site references many other sources from which its data were obtained. There is no assumption that the 38 countries are selected at random, and says that gun-related information tends to be unreliable. Additional data on guns per 100 population in 2014 were added by Anne Anderson from
lauren.bartschDec 5, 20153KB67
Baseball Hits and Homerunslauren.bartschNov 13, 2015951B159
Sample Proportions EE1 F15
231 sample proportions (p-hat values)
lauren.bartschNov 9, 20151KB18
Self-Survey Class Data (HPE 2071 - 002)lauren.bartschAug 31, 20151KB227
Happiness Data from GSS.xlslauren.bartschApr 23, 20156KB175
Comparing two drugs
The basic practice of statistics: instructor's edition. David S. Moore - William Notz - Michael A. Fligner - R. Scott Linder - W.H. Freeman and Co. – 2013 (p. 462) 18.50 Comparing two drugs. Makers of generic drugs must show that they do not differ significantly from the “reference” drugs that they imitate. One aspect in which drugs might differ is their extent of absorption in the blood. Table 18.6 gives data taken from 20 healthy nonsmoking male subjects for one pair of drugs. This is a matched pairs design. Numbers 1 to 20 were assigned at random to the subjects. Subjects 1 to 10 received the generic drug first, followed by the reference drug. Subjects 11 to 20 received the reference drug first, followed by the generic drug. In all cases, a washout period separated the two drugs so that the first had disappeared from the blood before the subject took the second. By randomizing the order, we eliminate the order in which the drugs were administered from being confounded with the difference in the absorption in the blood. Do the drugs differ significantly in the amount absorbed in the blood? Table 18.6 Absorption extent for two versions of a drug
lauren.bartschApr 22, 2015312B170
Body Measurementslauren.bartschApr 22, 20154KB142
house_selling_prices_FL.txtlauren.bartschApr 20, 20153KB55
Home prices in Albuquerquelauren.bartschApr 20, 20153KB67
House Selling Prices - Oregonlauren.bartschApr 20, 20158KB72
European Football dataset lauren.bartschApr 15, 20154KB281
Regression: Cigarettes Lung Kidney Leukemia Bladder
"Cigarette smoking and cancers of the urinary tract: Geographic variation in the United States" Journal of the National Cancer Institute (vol. 41, no. 5, November, 1968), pp. 1205-1211; table from pp. 1206-1207. Joseph F. Fraumeni, Jr. Oxford University Press Units: cigarettes sold per capita, cancer deaths per 100,000
lauren.bartschApr 9, 20152KB351
Cigarette Consumption vs CHD Mortalitylauren.bartschApr 9, 20151KB202
Cancer Survival
Patients with advanced cancers of the stomach, bronchus, colon, ovary or breast were treated with ascorbate. The purpose of the study was to determine if the survival times differ with respect to the organ affected by the cancer. Number of cases: 64 Reference:Cameron, E. and Pauling, L. (1978) Supplemental ascorbate in the supportive treatment of cancer: re-evaluation of prolongation of survival times in terminal human cancer. Proceedings of the National Academy of Science USA, 75, 4538Ð4542. Also found in: Manly, B.F.J. (1986) Multivariate Statistical Methods: A Primer, New York: Chapman & Hall, 11. Also found in: Hand, D.J., et al. (1994) A Handbook of Small Data Sets, London: Chapman & Hall, 255. [ANOVA , Boxplot , Transformation] Variable Description Survival Survival time (in days?) Organ Organ affected by the cancer
lauren.bartschApr 6, 2015753B393
Educational Spending
Description: Average salary paid to teachers and expenditures per pupil on education in the 50 states and the District of Columbia. Number of cases: 51 Reference: Moore, David S., and George P. McCabe (1989). Introduction to the Practice of Statistics [ANCOVA , ANOVA , Scatterplot] Variable Description State State Region Region Pay Amount of pay in thousands Spend Average amount spent per student in thousands
lauren.bartschApr 6, 2015987B262
Seating Choice versus GPA (For 3 rows, with Text and Indicator Columns)lauren.bartschApr 5, 20151KB155
Concussions by Sport and Sexlauren.bartschApr 5, 20152KB109
Weights of Football Players from 5 NFL Teamslauren.bartschApr 2, 2015425B107
Smoking, Age, and Income
% smoking is the percent of the population that are smokers income is in 5 categories age is in 3 categories
lauren.bartschApr 2, 2015163B279
Elderly Health Care Consumptionlauren.bartschMar 29, 2015192KB174
College Students: Work, Pets, Genderlauren.bartschMar 29, 20151KB152
Female HEALTH.xlslauren.bartschMar 29, 20152KB185
Asking prices for 4-bedroom homes in Bryan-College Station TXlauren.bartschMar 29, 2015877B163
Responses to opinions on global warming surveylauren.bartschMar 26, 201517KB20
Weight Loss Programlauren.bartschMar 26, 2015173B65
Alcohol data from adultslauren.bartschMar 26, 201510KB165
Guns Ownership and Deaths by Firearms by Statelauren.bartschMar 26, 20152KB248
College Worth It?lauren.bartschMar 26, 201517KB92
Paired Differences WBC Examplelauren.bartschMar 24, 2015422B127
Effect of Smoke on infantslauren.bartschMar 24, 20151KB250
Low Birth Weight Study
OURCE: Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition Data were collected at Baystate Medical Center, Springfield, Massachusetts during 1986. DESCRIPTIVE ABSTRACT: The goal of this study was to identify risk factors associated with giving birth to a low birth weight baby (weighing less than 2500 grams). Data were collected on 189 women, 59 of which had low birth weight babies and 130 of which had normal birth weight babies. Four variables which were thought to be of importance were age, weight of the subject at her last menstrual period, race, and the number of physician visits during the first trimester of pregnancy. LIST OF VARIABLES: Columns Variable Abbreviation ----------------------------------------------------------------------------- 2-4 Identification Code ID 10 Low Birth Weight (0 = Birth Weight >= 2500g, LOW 1 = Birth Weight < 2500g) 17-18 Age of the Mother in Years AGE 23-25 Weight in Pounds at the Last Menstrual Period LWT 32 Race (1 = White, 2 = Black, 3 = Other) RACE 40 Smoking Status During Pregnancy (1 = Yes, 0 = No) SMOKE 48 History of Premature Labor (0 = None 1 = One, etc.) PTL 55 History of Hypertension (1 = Yes, 0 = No) HT 61 Presence of Uterine Irritability (1 = Yes, 0 = No) UI 67 Number of Physician Visits During the First Trimester FTV (0 = None, 1 = One, 2 = Two, etc.) 73-76 Birth Weight in Grams BWT ----------------------------------------------------------------------------- PEDAGOGICAL NOTES: These data have been used as an example of fitting a multiple logistic regression model. STORY BEHIND THE DATA: Low birth weight is an outcome that has been of concern to physicians for years. This is due to the fact that infant mortality rates and birth defect rates are very high for low birth weight babies. A woman's behavior during pregnancy (including diet, smoking habits, and receiving prenatal care) can greatly alter the chances of carrying the baby to term and, consequently, of delivering a baby of normal birth weight. The variables identified in the code sheet given in the table have been shown to be associated with low birth weight in the obstetrical literature. The goal of the current study was to ascertain if these variables were important in the population being served by the medical center where the data were collected. References: 1. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).
lauren.bartschMar 24, 20156KB990
Top US Problemslauren.bartschFeb 25, 201534KB267
Gender/Eye Colorlauren.bartschOct 14, 201494KB355


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