Loading StatCrunch!

Please wait...

Please wait...

- StatCrunch Menu
- About
- Sign out
- Results
- Session
- Applets
- New Data Table
- Open statcrunch.com
- Developer

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).

Simple Linear Regression

**Simple linear regression results:**

Dependent Variable: LWT

Independent Variable: AGE

LWT = 105.66518 + 1.0392263 AGE

Sample size: 189

R (correlation coefficient) = 0.18007315

R-sq = 0.032426341

Estimate of error standard deviation: 30.159825

**Parameter estimates:**

**Analysis of variance table for regression model:**

By orbisorumarina

On Dec 2, 2013

Dependent Variable: LWT

Independent Variable: AGE

LWT = 105.66518 + 1.0392263 AGE

Sample size: 189

R (correlation coefficient) = 0.18007315

R-sq = 0.032426341

Estimate of error standard deviation: 30.159825

Parameter |
Estimate |
Std. Err. |
Alternative |
DF |
T-Stat |
P-Value |

Intercept | 105.66518 | 9.893097 | ≠ 0 | 187 | 10.680697 | <0.0001 |

Slope | 1.0392263 | 0.41512833 | ≠ 0 | 187 | 2.5033856 | 0.0132 |

Source |
DF |
SS |
MS |
F-stat |
P-value |

Model | 1 | 5700.5027 | 5700.5027 | 6.2669396 | 0.0132 |

Error | 187 | 170098.02 | 909.61506 | ||

Total | 188 | 175798.52 |

By orbisorumarina

On Dec 2, 2013

This data set has 17 favorites. Sign in to add it to your favorites!

Adding a data set to your favorites makes it easier to come back to in the future!

CommentsAlready a member? Sign in.

BygorgeouskinavenueMar 12, 2013

According to the table â€œLow Birth Weight Studyâ€ on StatCrunch, the youngest woman who was pregnant was 14 years of age, the oldest was 45 years old and the age mean was 23.24. Taking in consideration that some women smoked during pregnancy, which could affect the birth weight, 74 women smoked and 115 women did not smoke during their pregnancy. Thus said, slightly more than half of those women did not smoke which is a good thing, but another half did, which could affect the birth weight. However, if we take two 17 year olds and compared, one who smoked during pregnancy her babyâ€™s weight was 2414 and non smoking was 2438. On the other hand, an 18 year old who did not smoke, her babyâ€™s weight was 2282 and a smoking one, her baby weight was 2296 even though the initial weight between those two 18 year olds was very different. There is a lot of different examples like that in the table. However, that tells us that a lurking variable is involved here and plays a big role. One of them is family history which involves the obesity of the pregnant woman which leads to a bigger weight of the baby even though the mother smoked. On the other hand, if a pregnant one no matter of age smoked and her initial weight was normal and the babyâ€™s weight was bigger than the other baby whose mother never smoked and initial weight was beyond normal where could be another possibility that heavier one was a lot taller, which table does not tell us about. The table did not include weight, family history or a shoe size, which is a lurking variable. According to the scatter diagram, between motherâ€™s weight and the newborn weight 3 or 4 outliers were present which really did not make big of the difference and the parameter estimates fell into this equation: y(hat)=4.4293 2369.672 with coefficient correlation of 0.1858. Also, the pie chart gave a clear picture that 78% of pregnant women did not smoke and 22% of over women smoked. Whether pregnant woman smoked or not during the pregnancy, smoking does not affect the birth weight, but on the other hand, smoking, especially pregnancy isnâ€™t a healthy habit which may affect the birth weight and may give the bad habit of smoking to their offspring in the babyâ€™s future.