R models

> summary(mod_lie_j)

Call:
lm(formula = data[, 7] ~ lj + qq + data[, 1])

Residuals:
        1         2         3         4         5         6         7
-0.391519  1.204999 -1.194907 -0.302552 -0.255680 -0.001247  0.940906

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.253307   8.725644   5.530   0.0117 *
lj           0.911253   0.195983   4.650   0.0188 *
qq          -0.010194   0.002666  -3.823   0.0315 *
data[, 1]    2.005974   0.578837   3.466   0.0405 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.166 on 3 degrees of freedom
Multiple R-squared:  0.9036,	Adjusted R-squared:  0.8073
F-statistic: 9.376 on 3 and 3 DF,  p-value: 0.04929

> mod_lie<-lm(data[,7] ~ lj+qq)
> summary(mod_lie)

Call:
lm(formula = data[, 7] ~ lj + qq)

Residuals:
       1        2        3        4        5        6        7
-2.03372  0.29986  0.63260  0.04142 -2.15618 -0.11560  3.33162

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.384714   2.637232   6.971  0.00223 **
lj           0.455216   0.281335   1.618  0.18096
qq          -0.002991   0.003235  -0.925  0.40750
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.258 on 4 degrees of freedom
Multiple R-squared:  0.5178,	Adjusted R-squared:  0.2767
F-statistic: 2.148 on 2 and 4 DF,  p-value: 0.2325