Linear Regression

Model Fit Measures
			Overall Model Test
Model	R²	Adjusted R²	F	df1	df2	p
1	0.120	0.0695	2.38	4	70	0.05976

Model Coefficients - saving
Predictor	Estimate	SE	t	p
Intercept	-389.638	1474.6795	-0.264	0.79239
income	0.126	0.0553	2.282	0.02555
size	65.663	120.5137	0.545	0.58758
education	38.294	57.2443	0.669	0.50572
age	2.956	26.9692	0.110	0.91304

R

Call:
lm(formula = saving ~ income + size + education + age, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2509.4  -958.5  -339.7   263.8  4602.3 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -389.63835 1474.67951  -0.264   0.7924  
income         0.12627    0.05534   2.282   0.0256 *
size          65.66259  120.51371   0.545   0.5876  
education     38.29397   57.24433   0.669   0.5057  
age            2.95580   26.96923   0.110   0.9130  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1436 on 70 degrees of freedom
Multiple R-squared:  0.1198,	Adjusted R-squared:  0.06946 
F-statistic: 2.381 on 4 and 70 DF,  p-value: 0.05976

Call:
lm(formula = saving ~ income + size + education + age, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2509.4  -958.5  -339.7   263.8  4602.3 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept) -389.63835 1442.50308  -0.270   0.7879  
income         0.12627    0.06395   1.975   0.0523 .
size          65.66259  101.53832   0.647   0.5200  
education     38.29397   56.46033   0.678   0.4999  
age            2.95580   29.13543   0.101   0.9195  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1436 on 70 degrees of freedom
Multiple R-squared:  0.1198,	Adjusted R-squared:  0.06946 
F-statistic: 3.331 on 4 and 70 DF,  p-value: 0.01481