Results

Linear Regression

Model Fit Measures
ModelR
10.4550.207

 

Model Coefficients - y1
PredictorEstimateSEtp
Intercept0.8090.1555.230< .001
x0.7820.1555.057< .001

 

Assumption Checks

Normality Tests
 Statisticp
Shapiro-Wilk0.9790.118
Kolmogorov-Smirnov0.1100.174
Anderson-Darling0.9490.016
Note. Additional results provided by moretests

 

Heteroskedasticity Tests
 Statisticp
Breusch-Pagan15.971< .001
Goldfeld-Quandt1.0410.444
Harrison-McCabe0.4900.442
Note. Additional results provided by moretests

 

Linear Regression

Model Fit Measures
ModelR
10.7770.603

 

Model Coefficients - y2
PredictorEstimateSEtp
Intercept0.9210.0949.810< .001
x1.1450.09412.200< .001

 

Assumption Checks

Normality Tests
 Statisticp
Shapiro-Wilk0.9920.836
Kolmogorov-Smirnov0.0570.897
Anderson-Darling0.3460.476
Note. Additional results provided by moretests

 

Heteroskedasticity Tests
 Statisticp
Breusch-Pagan0.5690.451
Goldfeld-Quandt0.8430.722
Harrison-McCabe0.5400.710
Note. Additional results provided by moretests

 

R

	studentized Breusch-Pagan test

data:  y1 ~ x
BP = 16, df = 1, p-value = 6e-05

	Goldfeld-Quandt test

data:  y1 ~ x
GQ = 1, df1 = 48, df2 = 48, p-value = 0.4
alternative hypothesis: variance increases from segment 1 to 2

	studentized Breusch-Pagan test

data:  y2 ~ x
BP = 0.57, df = 1, p-value = 0.5

	Goldfeld-Quandt test

data:  y2 ~ x
GQ = 0.84, df1 = 48, df2 = 48, p-value = 0.7
alternative hypothesis: variance increases from segment 1 to 2

	studentized Breusch-Pagan test

data:  y1 ~ x
BP = 16, df = 1, p-value = 6e-05

	Goldfeld-Quandt test

data:  y1 ~ x
GQ = 1, df1 = 48, df2 = 48, p-value = 0.4
alternative hypothesis: variance increases from segment 1 to 2

	studentized Breusch-Pagan test

data:  y2 ~ x
BP = 0.57, df = 1, p-value = 0.5

	Goldfeld-Quandt test

data:  y2 ~ x
GQ = 0.84, df1 = 48, df2 = 48, p-value = 0.7
alternative hypothesis: variance increases from segment 1 to 2