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Question

Aplia: Student Question quiz?quiz actionstakeQuiz&quiz; probGuid-QNAPCOA80101000000391094 300b00008&ctx-dcullen-00018ick-m;, 151104572 8. Testing the significance of a multiple regression equation Hosmer and Lemeshow (Applied Logistic Regression, 2000, Hoboken: Wiley, 2nd edition, page 25) cte a study conducted at Baystate Medical Center in Springfield, Massachusetts, to identify fa birth to a low-birth-weight baby. Low birth weight is defined as weighing less than 2500 grams (S pounds, 8 ounces) at birth. Low-birth-weight babies have increased risk of health problems, disability, and death. Data were collected on 189 women, 59 of whom had low-birth-weight babies and 130 of whom had normal-birth-weight bables. The following is a subsample of the data for seven babies, focusing on the age and prepregnancy weights of the mothers as predictors of their babies' birth weights. ctors that affect the risk of giving Mother's Prepregnancy Birth Weight of Weight (Pounds) Baby (Grams) Mother's Age (Years) X1 16 29 29 19 20 17 24 Person 135 135 154 147 175 125 133 3643 3651 3651 3651 3600 3614 3614 Sum of Squares ss,1-17.00-u1711- ss-2876.00 Sum of Products SPXIY 261.00 SPXZY475.00 SPx1263.00 Find the regression equation for predicting Y given X. On the basis of your calculations, you find that bi is , ba is and the Y intercopt is Calculate R2, SSon and SSresc.as. R2 is

Explanation / Answer

Result:

b1=1.6034

b2=-0.3365

Intercept=3,644.9906

R square =0.201

SS regression=578.3342

SSresidual =2297.6658

Critical F at 0.05 level=6.94

Calculated F=0.50 which is < critical F.

Ho is not rejected.

The regression model is not significant.

Regression Analysis

R²

0.201

Adjusted R²

0.000

n

7

R

0.448

k

2

Std. Error

23.967

Dep. Var.

y

ANOVA table

Source

SS

df

MS

F

p-value

Regression

578.3342

2  

289.1671

0.50

.6383

Residual

2,297.6658

4  

574.4165

Total

2,876.0000

6  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=4)

p-value

95% lower

95% upper

Intercept

3,644.9906

88.9394

40.983

2.12E-06

3,398.0551

3,891.9261

x1

1.6034

1.8186

0.882

.4277

-3.4458

6.6527

x2

-0.3365

0.5831

-0.577

.5948

-1.9556

1.2826

Regression Analysis

R²

0.201

Adjusted R²

0.000

n

7

R

0.448

k

2

Std. Error

23.967

Dep. Var.

y

ANOVA table

Source

SS

df

MS

F

p-value

Regression

578.3342

2  

289.1671

0.50

.6383

Residual

2,297.6658

4  

574.4165

Total

2,876.0000

6  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=4)

p-value

95% lower

95% upper

Intercept

3,644.9906

88.9394

40.983

2.12E-06

3,398.0551

3,891.9261

x1

1.6034

1.8186

0.882

.4277

-3.4458

6.6527

x2

-0.3365

0.5831

-0.577

.5948

-1.9556

1.2826

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