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Showing 1 to 15 of 63 data sets matching birth weight
Data Set/Description Owner Last edited Size Views
North Carolina birth data
A Random Sample of 1000 births from the state of North Carolina. Plurarility refers to the number of children associated with the birth. Gender 1=Male, 2=Female. fage is age of father (years), mage is age of mother (years), visits is number of pre-natal medical visits, marital is 1=married, 2=unmarried, racemom is Race of Mother (0=Other Non-white, 1=White, 2=Black 3=American Indian, 4=Chinese, 5=Japanese, 6=Hawaiian, 7=Filipino, 8=Other Asian or Pacific Islander), hispmom is whether mother is of Hispanic origin (C=Cuban, M=Mexican, N=Non-Hispanic, O=Other and Unknown Hispanic, P=Puerto Rican, S=Central/South American, U=Not Classifiable), gained is weight gain during pregnancy (pounds), lowbw is if birth weight is 2500 grams or lower, tpounds is birthweight in pounds, smoke is 0=no, 1=yes for mother admitted to smoking, mature is 0=no, 1-yes for mother is 35 or older, premie is 0=no, 1=yes to being born 36 weeks or sooner.
jph422Sep 8, 200837KB5815
North Carolina premature births
A Random Sample of 1000 births from the state of North Carolina. Plurarility refers to the number of children associated with the birth. Gender 1=Male, 2=Female. fage is age of father (years), mage is age of mother (years), visits is number of pre-natal medical visits, marital is 1=married, 2=unmarried, racemom is Race of Mother (0=Other Non-white, 1=White, 2=Black 3=American Indian, 4=Chinese, 5=Japanese, 6=Hawaiian, 7=Filipino, 8=Other Asian or Pacific Islander), hispmom is whether mother is of Hispanic origin (C=Cuban, M=Mexican, N=Non-Hispanic, O=Other and Unknown Hispanic, P=Puerto Rican, S=Central/South American, U=Not Classifiable), gained is weight gain during pregnancy (pounds), lowbw is if birth weight is 2500 grams or lower, tpounds is birthweight in pounds, smoke is 0=no, 1=yes for mother admitted to smoking, mature is 0=no, 1-yes for mother is 35 or older, premie is 0=no, 1=yes to being born 36 weeks or sooner.
statcrunchhelpApr 10, 20144KB2357
nc2005birth300.xls
A Random Sample of 300 births from the state of North Carolina. Plurarility refers to the number of children associated with the birth. Gender 1=Male, 2=Female. fage is age of father (years), mage is age of mother (years), visits is number of pre-natal medical visits, marital is 1=married, 2=unmarried, racemom is Race of Mother (0=Other Non-white, 1=White, 2=Black 3=American Indian, 4=Chinese, 5=Japanese, 6=Hawaiian, 7=Filipino, 8=Other Asian or Pacific Islander), hispmom is whether mother is of Hispanic origin (C=Cuban, M=Mexican, N=Non-Hispanic, O=Other and Unknown Hispanic, P=Puerto Rican, S=Central/South American, U=Not Classifiable), gained is weight gain during pregnancy (pounds), lowbw is if birth weight is 2500 grams or lower, tpounds is birthweight in pounds, smoke is 0=no, 1=yes for mother admitted to smoking, mature is 0=no, 1-yes for mother is 35 or older, premie is 0=no, 1=yes to being born 36 weeks or sooner.
jph422Nov 5, 200711KB981
Effect of Smoke on infants
Data was collected by a random survey of mothers in KY through a dance studio during November 2010 by SABRINA LAFFERTY & KAREN HOLLAND (ST 291 Fall 2010 candidates at HCTC) as a requirement for semester project. They asked 57 mothers about the gestation period for their pregnancies, the birth weight, the length of their newborns and whether they smoked while they were pregnant.
statcrunchhelpMar 6, 20141KB7395
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, 20126KB7980
STA 220_Effect of Smoke on infants
Data was collected by a random survey of mothers in KY through a dance studio during November 2010 by SABRINA LAFFERTY & KAREN HOLLAND (ST 291 Fall 2010 candidates at HCTC) as a requirement for semester project. They asked 57 mothers about the gestation period for their pregnancies, the birth weight, the length of their newborns and whether they smoked while they were pregnant.
mnyagol0001Jan 12, 20131KB1995
Elephant Birth weight (lbs)
Birth Weights for elephants born in captivity.
pperryDec 6, 20178KB870
MothersOfNewborns.xlsx
This data set records data about 189 mothers of newborns, along with data about their infant. 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). The data were collected at Baystate Medical Center, Springfield, MA, in 1986. [quoted from description at http://www.umass.edu/statdata/statdata/data/lowbwt.txt You must include the following citation in your report as the source of the data, or you will be in violation of copyright laws: Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition. These data are copyrighted by John Wiley & Sons Inc. and must be acknowledged and used accordingly. Data were collected at Baystate Medical Center, Springfield, Massachusetts during 1986.
anderson_instructorSep 1, 20178KB1185
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, 20156KB986
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).
shleeApr 11, 20166KB781
VeryLowBirthWeightInfants.xlsx
This data set includes data on 671 infants who had a very low birth weight, defined as being less than 1600 grams. The data were collected between 1981 and 1987 at Duke University Medical Center by Dr. Michael O'Shea, now of Bowman Gray Medical Center. This project was funded by a Clinical Epidemiology Grant from the Mellon Foundation. Note that ALL these infants had very low birth weights, so if you are comparing infants with a relatively high birth weight to infants with a relatively low birth weight, even the relatively high birth weights are very low compared to normal infant birth weights. Results of the study were published in M. O'Shea, D.A. Savitz, M.L. Hage, K.A. Feinstein: Prenatal events and the risk of subependymal / intraventricular haemorrhage in very low birth weight neonates. Paediatric and Perinatal Epdiemiology 1992;6:352-362.
anderson_instructorSep 1, 201743KB569
Patriots Roster
we're using the new england patriots roster. the variable that we will be using are number, height, weight, birth year, and position. our objective is to chart position using all of the other variables to compare the relation between a players position and all other attributes that are relative to the position.
emacduffMay 3, 20105KB434
Births in Philadelphia
Births in Philadelphia. These are data based on a 5% sample of all births occurring in Philadelphia in 1990. The sample has 1115 observations (after deleting 32 cases with incomplete information) on five variables): black = yes, no educ = Mother's years of education smoke = yes, no gestate = gestational age in week birthweight = birth weight in grams Reference: I. T. Elo, G. Rodruez and H. Lee (2001). Racial and Neighborhood Disparities in Birthweight in Philadelphia. Paper presented at the Annual Meeting of the Population Association of America, Washington, DC 2001.
jmantheyApr 10, 201424KB459
Time of Birth, Sex, and Birth Weight of 44 Babies
NAME: Time of Birth, Sex, and Birth Weight of 44 Babies TYPE: Observational SIZE: 44 observations, 4 variables DESCRIPTIVE ABSTRACT: The dataset contains the time of birth, sex, and birth weight for each of 44 babies born in one 24-hour period at a Brisbane, Australia, hospital. Also included is the number of minutes since midnight for each birth. SOURCE: The data appeared in the Brisbane newspaper _The Sunday Mail_ on December 21, 1997. VARIABLE DESCRIPTIONS: Columns 1 - 8 Time of birth recorded on the 24-hour clock 9 - 16 Sex of the child (1 = girl, 2 = boy) 17 - 24 Birth weight in grams 25 - 32 Number of minutes after midnight of each birth Values are aligned and delimited by blanks. There are no missing values. STORY BEHIND THE DATA: Forty-four babies -- a new record -- were born in one 24-hour period at the Mater Mothers' Hospital in Brisbane, Queensland, Australia, on December 18, 1997. For each of the 44 babies, _The Sunday Mail_ recorded the time of birth, the sex of the child, and the birth weight in grams. Additional information about these data can be found in the "Datasets and Stories" article "A Simple Dataset for Demonstrating Common Distributions" in the _Journal of Statistics Education_ (Dunn 1999). PEDAGOGICAL NOTES: The data can be used to demonstrate fitting the binomial distribution (the number of boys/girls born out of 44 births), the geometric distribution (the number of births until a boy/girl is born), the Poisson distribution (births per hour for each hour), and the exponential distribution (times between births). The normal distribution is found to be unsuitable for modeling the birth weights, but better results are obtained when birth weights are separated by sex. The dataset can also be used to illustrate hypothesis tests about proportions, comparisons of birth weights by gender, the runs test of randomness of gender, and skewed data. REFERENCE: Steele, S. (December 21, 1997), "Babies by the Dozen for Christmas: 24-Hour Baby Boom," _The Sunday Mail_ (Brisbane), p. 7. SUBMITTED BY: Peter K. Dunn Department of Mathematics and Computing University of Southern Queensland Toowoomba, Queensland, Australia 4350 dunn@usq.edu.au
hancocdMar 18, 20172KB386
Baby Data
NAME: Time of Birth, Sex, and Birth Weight of 44 Babies TYPE: Observational SIZE: 44 observations, 4 variables DESCRIPTIVE ABSTRACT: The dataset contains the time of birth, sex, and birth weight for each of 44 babies born in one 24-hour period at a Brisbane, Australia, hospital. Also included is the number of minutes since midnight for each birth. SOURCE: The data appeared in the Brisbane newspaper _The Sunday Mail_ on December 21, 1997. VARIABLE DESCRIPTIONS: Columns 1 - 8 Time of birth recorded on the 24-hour clock 9 - 16 Gender of the child (1 = girl, 2 = boy) 17 - 24 Birth weight in grams 25 - 32 Number of minutes after midnight of each birth
ds-12%scAug 11, 20082KB381

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