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Showing 1 to 15 of 180 data sets matching WOMEN
Data Set/Description Owner Last edited Size Views
Titanic Survival Data
This has the survival data for the passengers on the Titanic. It also has if they were an adult or child, their gender, and the class they were staying on.
statcrunch_featuredApr 3, 201748KB3905
Body Temperature
Data taken from the Journal of Statistics Education online data archive. That archive in turn got the data from an article in the Journal of the American Medical Association. (Mackowiak, et al., "A Critical Appraisal of 98.6 Degrees F …", vol. 268, pp. 1578-80, 1992).
"Body Temp" is measured in degrees fahrenheit
"Heart rate" is the resting beats per minute
statcrunch_featuredJun 27, 20172KB11749
WHO Health Data v4.xlsx
Country Country, Region WHO_region, AlcConsumption Total (recorded + unrecorded) adult (15+ years) per capita consumption projected estimates for 2008_2008 BAC_limit Blood Alcohol Concentration (BAC) limit for drivers - general - 2011, bednet Women that slept under a bednet last night (%), bednet_yr bednet Year, drinkWater_R Population using improved drinking-water sources (%)_Rural_2011, drinkWater_U Population using improved drinking-water sources (%)_Urban_2011, healthcenters Total density per 100 000 population: Health centres, healthposts Total density per 100 000 population: Health posts, Hiv_AidsDeaths Deaths due to HIV/AIDS (per 100 000 population)_2011, HivAdults Prevalence of HIV among adults aged 15 to 49 (%)_2011, hospital_yr hospital Year of data collection, hospitals Total density per 100 000 population: Hospitals, LifeExp_60_F Life expectancy at age 60 (years)_Female_2011, LifeExp_60_M Life expectancy at age 60 (years)_Male_2011, LifeExp_Birth_F Life expectancy at birth (years)_Female_2011, LifeExp_Birth_M Life expectancy at birth (years)_Male_2011, NumRegVehicles Number of Registered Vehicles Nursing_Midwives, Nursing_and_midwifery_personnel_density__per_1000_population_, Physicians Physicians_density__per_1000_population_, pollution Outdoor air pollution (Annual PM10 [ug/m3]), polYear Year, RegVehYear Year, rural_hosp Total density per 100 000 population: District/rural hospitals, sanFacility_R Population using improved sanitation facilities (%)_Rural_2011, sanFacility_U Population using improved sanitation facilities (%)_Urban_2011, seat_belt_drivers Seat-belt wearing rate (%) Driver only_2011, sex_work_syph Sex workers with active syphilis (%), sex_work_syph_yr sex_work_syph Year of data collection, spec_hosp Total density per 100 000 population: Specialized hospitals, Tobacco_S_F Current smoking of any tobacco product (age-standardized rate)_Female_2009, Tobacco_S_M Current smoking of any tobacco product (age-standardized rate)_Male_2009, Tobacco_Y_F Current users of any tobacco product (youth rate)_Female_2010, Tobacco_Y_M Current users of any tobacco product (youth rate)_Male_2010, TrafDeathRate Estimated road traffic death rate (per 100 000 population)_2010, TrafDeaths Estimated number of road traffic deaths _2010, UVradiation UV radiation_2004.
swhardyDec 6, 201531KB7143
Height Difference of Men and Women by country
This data is about the difference in heights of men and women by country all across the world. The quantitative data being observed is the average height of Men and Women by country, The year at which the average heights were obtained, and the population/age ranges. The categorical data being observed is the country and methodology. The reason for why I chose this set of data is because it was interesting and I thought why not do an observation on something I know not much about.
pv31283923Feb 11, 20117KB1228
Low Birth Weight Study
SOURCE: 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).
wikipetersonJul 23, 20126KB7199
Predicting_Intelligence
The following explains the variables in the data: sex: Gender of the individual in the photo age: Age of the individual in the photo perceived intelligence (ALL): Mean z-score of the perceived intelligence of all 160 raters perceived intelligence (WOMEN): Mean z-score of the perceived intelligence of the female raters perceived intelligence (MEN): Mean z-score of the perceived intelligence of the male raters attractiveness (ALL): Mean z-score of the attractiveness rating of all160 raters attractiveness (MEN): Mean z-score of the attractiveness rating of the male raters attractiveness (WOMEN): Mean z-score of the attractiveness rating of the female raters IQ: Intelligence quotient based on the Czech version of Intelligence Structure Test
msullivan13803Jul 14, 20145KB1159
Heights of Females
This data set contains the heights in inches for 428 women taking an introductory statistics course at a Midwestern college. This dataset was originally created by Dr. Jim Albert.
statcrunchhelpOct 28, 20141KB2380
Project Data
This data encompasses the results of surveying 50 of my women friends, who exercise regularly, in order to determine the average resting heart-rate of females who exercise regularly. I define exercising "regularly" as doing a set aside workout of at least 20 minutes, at least 4 times a week. For my research I stayed within an age group range of females ages 18-22 years old.
jjindrich0127Jul 15, 20101KB1945
Word Counts by Males and Females Lab 2
Word counts during conversation of Men vs. Women to answer the research question: "Are Women Really More Talkative than Men?"
bdodson94Oct 26, 20174KB886
Jealousy file.xlsx
Do men and women differ in jealousy about their romantic partners? Research by Buss, Larsen, Westen, and Semmelroth (Exp. 1, 1992) suggested that the answer is yes. In that study, heterosexual men and women in the United States imagined their romantic partners engaged in emotional or sexual affairs with another person, and then indicated which scenario would be more upsetting to them. Men reported being more distressed when imagining their partners involved in sexual infidelity, whereas women were more distressed when they imagined their partners involved in emotional infidelity. Buss et al. concluded that their findings supported their hypotheses, which were derived from evolutionary theory. Subsequent research either supported the Buss et al. (1992) findings or found limitations to their conclusions (Harris, 2003). For example, although Buss et al. used a forced-choice method in their study (e.g., “Which of these two scenarios is more upsetting?”), others have not found such clear sex differences when rating scales are used instead (DeSteno, Bartlett, Braverman, & Salvoes, 2002). In addition, cultural differences have also been found. For example, European and Asian men are more likely to choose emotional infidelity as worse, compared to American men (Harris, 2004). The purpose of this study was to see if (a) we would replicate the original Buss et al. (1992) findings using an Australian sample in 2015, and (b) whether asking participants to rate their feelings would reveal the same sex differences that were reported in the original work. We therefore had separate hypotheses regarding the differences between men and women with respect to emotional infidelity and sexual infidelity.
e.vanmanMay 7, 20177KB980
domviol.xls
This data was a pilot study of health outcomes related to domestic violence. Women from domestic violence shelters were asked about the severity of symptoms experienced in the last year. Women who were not abused were often workers at domestic violence shelters. The severity of the emotional, sexual, and physical violence was categorized into four groups 0 - no abuse, 1=least abuse, 2=middle level, and 3 = most. Abused is a dummy for whether abuse occurred. The main outcome variables are dummy variables for whether the woman experienced the health malady often in the last year. sxllhead=severe headaches, sxlinsom=insomnia, sxlchest=chest pain, sxlpelv=pelvic pain, sxlstom=stomach pain, sxlchok=sensation of choking, sxlbrea=shortness of breath,sxlvag=vaginal infection, sxlfat = fatigue. The women were age 18 - 48. Age25-34 is a dummy variable for age 25-34 and age35p is a dummy for ages 35 - 48. The variables least, middle, and most are dummy variables for level of violence. [Note: there are no easy labels for severity of violence. The label "Least" in no way implies that such a level is not important nor does it imply that there are no serious negative consequences on the life of the women experiencing the violence.] Creating contingency tables of abuse status vs. the health maladies is excellent practice for conditional probability and calculating a prevalence ratio to compare the two probabilities.
jph422Oct 15, 20086KB753
Academic Progress Rates
Academic Progress Rates (APR) 2004-2005 to 2012-2013 for football, men's basketball, women's basketball, baseball in the 10 Division 1 football conferences: the "Power Five" (Atlantic Coast, Big Ten, Big 12, Pac 12, Southeast) and Conference USA, Mid-American, Mountain West, Sun Belt, and American Athletic (formerly the Big East). All Division 1 men's lacrosse APR's are also included.
treilandMay 16, 2014398KB521
Women's Heptathlon 2004
Data from 2004 Women's Olympic Heptathlon
cdcummings12Aug 13, 2009843B475
Right out of College Women Make About $3 Less Per Hour than Men
Young men (age 21–24) with a college degree are paid an average hourly wage of $20.87 early in their careers; their female counterparts are paid an average hourly wage of just $17.88
treilandJun 7, 20175KB427
Sullivan_SIDUD4_04_Table_4 Colas Bone Density
Based on data obtained from Katherine L. Tucker et. al., "Colas, but not other carbonated beverages, are associated with low bone mineral density in older women: The Framingham Osteoporosis Study." American Journal of Clinical Nutrition 2006, 84:936-942.
phil_larsonSep 22, 2013224B508

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