KORELASI DAN REGRESI LINEAR - Diponegoro University

KORELASI DAN REGRESI LINEAR HERTANTO WAHYU SUBAGIO. Korelasi ... - regresi sederhana : ... Linear regression...

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KORELASI DAN REGRESI LINEAR

HERTANTO WAHYU SUBAGIO

Korelasi * menunjukkan arah hubungan * Uji : r product moment Pearson Spearman Kendall * Nilai : -1 s/d +1

Correlation between Plasma volume and body weight in 8 healthy men

Subject 1. 2 2. 3. 4. 5. 6. 7. 8.

Body weight (kg) 58.0 70 70.0 0 74.0 63.5 62.0 70.5 71.0 66.0

Plasma vol. (l) 2.75 2 2.86 86 3.37 2.76 2.62 3.49 3.05 3.12

Scatter Plot

Some possibilities in scatter plot

Formula

SPSS output Correlations

Tekanan sistolik (mm Hg)

Tekanan diastolik (mm Hg)

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

Tekanan Tekanan sistolik diastolik (mm Hg) (mm Hg) 1 000 1,000 ,814 814** , ,000 30 30 ,814** ,814 1,000 ,000 , 30 30

**.. Correlation is significant at the 0 0.01 01 level (2-tailed) (2-tailed).

X mempengaruhi Y ?

Diagram Pencar 200

180

Teka anan sistolik (m mm Hg)

160

140

120

100 50

60

70

80

Tekanan diastolik (mm Hg)

90

100

110

120

130

Regresi * Mampu memprediksi DV dari perubahan IV * Uji : - regresi sederhana : Y Y=a+bX. a+bX. - regresi majemuk : Y = a + b1X1 + b2X2 ……….biXi * Tidak otomatis menunjukkan sebab akibat

Linear regression g •Gives the equation of the straight line that best describes it and enables the prediction of one variable from the other

The equation is : y = a + bx a = intercept b = slope = regression coefficient y = dependent var var. x = independent var. The values for a and b are calculated so as to minimize the sum of the squared vertical distances of the points from the line. This is called a least square fit.

Linear Regression

Line

The intercept and slope of regression equation y=a+bx

Plasma vol=0.086+0.0.044 X bodyweight

SPSS Output : • • • • • •

Descriptive statistics Correlations Variables entered/removed Model summary Anova Coefficients

Descriptive Statistics

Tekanan sistolik (mm Hg) Tekanan diastolik (mm Hg)

Mean M 141,67

Std Deviation Std. D i ti 20,57

85 67 85,67

15 13 15,13

N 30 30

Correlations

Pearson Correlation

Sig. (1-tailed)

N

r = 0.814

Tekanan sistolik (mm Hg) Tekanan diastolik (mm Hg) Tekanan sistolik (mm Hg) Tekanan diastolik (mm Hg) Tekanan sistolik (mm Hg) Tekanan diastolik (mm Hg)

Tekanan sistolik (mm Hg) 1,000

Tekanan diastolik (mm Hg) ,814

,814

1,000

,

,000

,000

,

30

30

30

30

Variables Entered/Removedb

Model 1

Variables Entered Tekanan diastolik a (mm Hg)

Variables Removed

Method ,

Enter

a. All requested variables entered entered. b. Dependent Variable: Tekanan sistolik (mm Hg)

Model Summary

Model 1

R R Square ,814a ,662

Adjusted Adj sted R Square ,650

Std. Error of Std the Estimate 12,17

a. Predictors: (Constant), Tekanan diastolik (mm Hg)

R2 = Koefisien determinasi = Sumbangan var bebas terhadap kejadian var dependent 66 2% kejadian var dependent ditentukan oleh var bebas 66.2%

ANOVAb Sum of Model Squares 1 Regression 8121,557 Residual 4145,109 Total 12266,667

df

Mean Square 1 8121,557 28 148,040 29

F 54,861

Sig. ,000a

a. Predictors: (Constant), Tekanan diastolik (mm Hg) b Dependent Variable: Tekanan sistolik (mm Hg) b.

F hitung = 54.89, p = 0.000 Model regresi dapat dipakai untuk memprediksi var dependent

Coefficientsa

Model 1

(Constant) Tekanan diastolik (mm Hg)

Standardi zed Unstandardized U sta da d ed Coefficien Coe ce Coefficients ts B Std. Error Beta 46,900 12,986 1,106

,149

,814

t 3,612

Sig. ,001

7,407

,000

a. Dependent p Variable: Tekanan sistolik ((mm Hg) g)

Y = 46.9 + 1.106 X Sistole = 46.9 + 1.106 diastole

X mempengaruhi hi Y :

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