>
> summary(lm(ctab4[1,] ~ ctab4[5,]))� # Emotion Linear Model
Call:
lm(formula = ctab4[1, ] ~ ctab4[5, ])
Residuals:
������ 1������� 2�������
3������� 4������� 5�������
6
�0.51817
-0.83463� 0.50589� 0.07016 -0.91028� 0.65070
Coefficients:
�����������
Estimate Std. Error t value Pr(>|t|)
(Intercept)��
0.1094���� 0.7327�� 0.149���
0.889
ctab4[5, ]��
-0.0356���� 0.1882� -0.189���
0.859
Residual standard error: 0.7871 on 4 degrees of freedom
Multiple R-Squared: 0.008871,�� Adjusted R-squared: -0.2389
F-statistic: 0.0358 on 1 and 4 DF,� p-value: 0.8591
>
> summary(lm(ctab4[2,] ~ ctab4[5,]))� # Feeding Linear Model
Call:
lm(formula = ctab4[2, ] ~ ctab4[5, ])
Residuals:
������ 1������� 2�������
3������� 4������� 5�������
6
�0.10108� 0.04046 -0.84647� 0.62469�
0.62276 -0.54253
Coefficients:
�����������
Estimate Std. Error t value Pr(>|t|)
(Intercept)��
1.1297���� 0.6246�� 1.809���
0.145
ctab4[5, ]��
-0.3231���� 0.1604� -2.015���
0.114
Residual standard error: 0.671 on 4 degrees of freedom
Multiple R-Squared: 0.5036,���� Adjusted R-squared: 0.3795
F-statistic: 4.058 on 1 and 4 DF,� p-value: 0.1142
>
> summary(lm(ctab4[4,] ~ ctab4[5,]))� # Parenting Linear Model
Call:
lm(formula = ctab4[4, ] ~ ctab4[5, ])
Residuals:
����� 1������ 2������
3������ 4������ 5������
6
-0.2058� 0.4115
-0.3859� 0.1841� 0.1728 -0.1766
Coefficients:
�����������
Estimate Std. Error t value Pr(>|t|)��
(Intercept) -1.51948���
0.31417� -4.836� 0.00842 **
ctab4[5, ]�� 0.43873��� 0.08067��
5.438� 0.00555 **
---
Signif. codes:� 0
'***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3375 on 4 degrees of freedom
Multiple R-Squared: 0.8809,���� Adjusted R-squared: 0.8511
F-statistic: 29.58 on 1 and 4 DF,� p-value: 0.005549
>
> summary(lm(ctab4[3,1:3] ~ ctab4[5,1:3]))� # SoSex Young Linear Model
Call:
lm(formula = ctab4[3, 1:3] ~ ctab4[5, 1:3])
Residuals:
������ 1������� 2�������
3
-0.07539� 0.15078
-0.07539
Coefficients:
���������� ���Estimate Std. Error t value Pr(>|t|)
(Intercept)���
-0.6277���� 0.2821� -2.225���
0.269
ctab4[5, 1:3]��
0.4899���� 0.1306�� 3.752���
0.166
Residual standard error: 0.1847 on 1 degrees of freedom
Multiple R-Squared: 0.9337,���� Adjusted R-squared: 0.8674
F-statistic: 14.08 on 1 and 1 DF,� p-value: 0.1658
>
> summary(lm(ctab4[3,4:6] ~ ctab4[5,1:3]))� # SoSex Old Linear Model
Call:
lm(formula = ctab4[3, 4:6] ~ ctab4[5, 1:3])
Residuals:
����� 4������ 5������
6
-0.1733� 0.3466
-0.1733
Coefficients:
�������������
Estimate Std. Error t value Pr(>|t|)
(Intercept)���
-1.1390���� 0.6485� -1.756���
0.329
ctab4[5, 1:3]��
0.3937���� 0.3002�� 1.311���
0.415
Residual standard error: 0.4245 on 1 degrees of freedom
Multiple R-Squared: 0.6323,���� Adjusted R-squared: 0.2647
F-statistic:� 1.72
on 1 and 1 DF,� p-value: 0.4147
>
> ctab4
��������������
1��������� 2��������� 3���������� 4����������� 5��������� 6
Emotn� 0.5919764
-0.7964215� 0.5084993� 0.03717099 -0.978872433� 0.5465149
Feedg�
0.9076972� 0.5239615
-0.6860706� 0.46198236� 0.136940703 -1.3514592
SoSex -0.2131115�
0.5030030� 0.7667847 -0.91865458
-0.005071878 -0.1313330
Prntg -1.2865621 -0.2305431 -0.5892135� 0.41950122�
0.847003607� 0.9362774
cntr��
1.0000000� 2.0000000� 3.0000000�
4.00000000� 5.000000000� 6.0000000
> matplot(t(ctab4[1:4,]))