A study conducted at Baystate Medical Center in Springfield, Massachusetts, identified factors that affect the risk of giving birth to a low-birth-weight baby. Low birth weightis defined as weighing fewer than 2,500 grams (5 pounds, 8 ounces) at birth. Low-birth-weight babies have increased risk of health problems, disability, and death. [Source: Hosmer, D., & Lemeshow, S. (2000). Applied logistic regression(2nd ed.). Hoboken, NJ: Wiley.]Suppose that you conduct a similar study focusing on the age, prepregnancy weight, and weight gain of the mothers as predictors of their babies' birth weight among 54 low-birth-weight babies. You use a statistical software package to run a multiple regression predicting birth weight in grams (BIRTHWT) from the mother's age in years (AGE), the mother's prepregnancy weight in pounds (MOMWT), and the mother's weight gain in pounds (GAIN). Use the output that follows to answer the following questions.Correlations

		BIRTHWT	AGE	MOMWT	GAIN
BIRTHWT	Pearson Correlation	1.000	-0.3799	-0.1164	0.1925
	Sig. (two-tailed)		0.0046	0.4020	0.1630
	N	54	54	54	54
AGE	Pearson Correlation	-0.3799	1.000	0.1643	-0.0864
	Sig. (two-tailed)	0.0046		0.2352	0.5345
	N	54	54	54	54
MOMWT	Pearson Correlation	-0.1164	0.1643	1.000	-0.1404
	Sig. (two-tailed)	0.4020	0.2352		0.3113
	N	54	54	54	54
GAIN	Pearson Correlation	0.1925	-0.0864	-0.1404	1.000
	Sig. (two-tailed)	0.1630	0.5345	0.3113
	N	54	54	54	54

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	0.414	0.171	0.121	323.171

a. Predictors (constant): AGE, MOMWT, GAINANOVAbb

Model	Sum of Squares	df	Mean Square	F	Two-Tailed Sig.
1	Regression	1078632.4	3	359544.1	3.443	0.0235
	Residual	5221989.3	50	104439.8
	Total	6300621.6	53

a. Predictors (constant): AGE, MOMWT, GAINb. Dependent Variable: BIRTHWTCoefficients

Model		Unstandardized Coefficients	Standardized Coefficients	t	Two-Tailed Sig.
B	Std. Error	Beta
1	(Constant)	2632.1972	324.9535		8.1002	0.0000
	AGE	-27.9660	10.1444	-0.3606	-2.7568	0.0081
	MOMWT	-0.4462	1.6694	-0.0352	-0.2673	0.7904
	GAIN	4.9680	4.1377	0.1565	1.2007	0.2355

a. Dependent Variable: BIRTHWTThe estimated regression equation is:

= AGE + MOMWT + GAIN +

Following is part of a write-up of your results. Using the information in the table just given, fill in the blanks with the appropriate words or phrases that correctly describe the results of your study. Assume that you have nondirectional null hypotheses and are using a significance level of 0.05.

Multiple regression was used to determine whether low-birth-weight babies' birth weights could be predicted from their mothers' ages, prepregnancy weights, and weight gain over the course of the pregnancy. The overall regression was [F(3, 50) = , p = , and R = ]. Only was a significant predictor of birth weight (t= , p = ).

Suppose that the coefficients of all of the predictor variables you included in the regression model were significantly different from zero (that is, all three are significant). Which of the following comparisons might best help you think about which variable is the most important predictor?

Comparing the significance levels of the coefficients

Comparing the magnitudes of the beta values (the standardized coefficients)

Comparing the magnitudes of the standard errors of the coefficients

Comparing the magnitudes of the B values (the unstandardized coefficients)