BIRTH WEIGHT AND MATERNAL CHARACTERISTICS

Assessing Birth Weight and Maternal Characteristics

Assessing Birth Weight and Maternal Characteristics

Introduction

The study is related to assessing the birth weight with the maternal characteristics. However, the study particularly focuses on the maternal characteristics that include gestation length, sex of the child and the age of the mother, and the factors like smoking and birth weight.

Hypotheses

HO 1: There is an association of birth weight with the smoking.

HO 2: There is an association of birth weight with the sex of the child, gestation length and the age of mother.

HO 3: There is an association of smoking with the sex of the child, gestation length and the age of mother.

HO 4: There is no significant difference between the birth weight and sexes.

Results and Discussion

Smoking and Birth Weight

Descriptive Statistics

Mean

Std. Deviation

N

smoke

2.9515

5.66096

206

birth weight

3355.2087

518.61750

206

Correlations

smoke

birth weight

Pearson Correlation

smoke

1.000

-.274

birth weight

-.274

1.000

Sig. (1-tailed)

smoke

.

.000

birth weight

.000

.

N

smoke

206

206

birth weight

206

206

The above result for the data shows that there is correlation between smoking and birth weight. Moreover, the value of the Pearson correlation coefficient shows that the value is in negative, indicating the negative correlation of smoking with the birth weight.

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1

df2

Sig. F Change

1

.274a

.075

.071

5.45743

.075

16.575

1

204

.000

a. Predictors: (Constant), birth weight

b. Dependent Variable: smoke

From the table of model summary, it is found that the value of R-square is 7.5 % and the value of adjusted R-square is 7.1 % that means there is a relationship between the dependent variable that is smoking with the independent variable that is birth weight. In a simple regression analysis there response or dependent variable (y) can be the number of species, abundance or presence-absence of a single species and an explanatory or independent variable (x). The purpose is to obtain a simple function of the independent variable that is capable of describing as closely as possible the variation of the dependent variable. As the observed values of the dependent variable generally differ from those predicted by the feature, it has an error. The most effective role is one that describes the dependent variable with the lowest possible error or, in other words, with the smallest difference between the observed and predicted values. The difference between the observed and predicted values (the error function) is called residual variation or waste. To estimate the parameters of the function using the least squares fit. That is, it tries to find the function in which the sum of the squares of the differences between observed and expected values is less. However, with this type of strategy is required that the waste or errors are distributed normally and similarly to vary throughout the range of values of the dependent variable. These assumptions can be tested by examining the distribution of residuals and their relationship with the dependent variable. Furthermore, to know the further effect of smoking on birth weight, it is important to consider the following table;

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

493.662

1

493.662

16.575

.000b

Residual

6075.852

204

29.784

Total

6569.515

205

a. Dependent Variable: smoke

b. Predictors: (Constant), birth weight

The above table of regression analysis is reflecting that the significance value of analysis of variance table is under ...