Using SPSS for Simple Regression

UDP 520 Lab 6Lin Lin

November 27th, 2007

Dataset – WLTP

• 1000 adults aged 18+ (males and females) were recruited to study the effectiveness of Weight Loss Training Program (WLTP)

• Variables– Sex (female=1)– BMI_1(before WLTP)– BMI_2(after WLTP)– Urban or suburban (urban=1)– Overweight_1 (overweight before WLTP) (overweight=1)– Overweight_2 (overweight after WLTP) (overweight=1)

http://courses.washington.edu/urbdp520/UDP520/WLTP.sav

Outline

• Dataset

• Using SPSS for Simple Regression

• Using SPSS for OLS test

Research Question

• How does BMI_2 relate to BMI_1?

• Predict BMI after people participated WLTP.

Analysis

• Simple regression

• Where y : BMI_2 x1: BMI_1

0 1 1y x

Using SPSS for Regression Analysis

SPSS Output

Model Summaryb

.657a .432 .431 1.47943Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), BMI_1a.

Dependent Variable: BMI_2b.

ANOVAb

1660.355 1 1660.355 758.598 .000a

2184.337 998 2.189

3844.691 999

Regression

Residual

Total

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), BMI_1a.

Dependent Variable: BMI_2b.

Coefficientsa

-1.015 .893 -1.137 .256 -2.766 .737

1.024 .037 .657 27.543 .000 .951 1.097

(Constant)

BMI_1

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig. Lower Bound Upper Bound

95% Confidence Interval for B

Dependent Variable: BMI_2a.

Residuals Statisticsa

19.3296 27.6322 23.5340 1.28919 1000

-4.23809 4.74759 .00000 1.47869 1000

-3.261 3.179 .000 1.000 1000

-2.865 3.209 .000 .999 1000

Predicted Value

Residual

Std. Predicted Value

Std. Residual

Minimum Maximum Mean Std. Deviation N

Dependent Variable: BMI_2a.

SPSS Output (cont.)

Using SPSS for OLS Test

SPSS Output (OLS Test)