�
>
> summary(lm(ctab4[1,] ~ ctab4[5,]))� # Emotion Linear Model
Call:
lm(formula = ctab4[1, ] ~ ctab4[5, ])
Residuals:
������ 1������� 2�������
3������� 4������� 5�������
6
�0.29144 -0.02437
-0.34639 -0.10334 -0.11392� 0.29656
Coefficients:
�����������
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.21620���
0.26213� -0.825��� 0.456
ctab4[5, ]��
0.05230��� 0.06731�� 0.777���
0.481
Residual standard error: 0.2816 on 4 degrees of freedom
Multiple R-Squared: 0.1311,���� Adjusted R-squared: -0.08608
F-statistic: 0.6037 on 1 and 4 DF,� p-value: 0.4806
>
> summary(lm(ctab4[2,] ~ ctab4[5,]))� # Feeding Linear Model
Call:
lm(formula = ctab4[2, ] ~ ctab4[5, ])
Residuals:
������ 1������� 2�������
3������� 4������� 5�
������6
-0.16819�
0.07559� 0.40395 -0.18973
-0.29382� 0.17220
Coefficients:
�����������
Estimate Std. Error t value Pr(>|t|)��
(Intercept)�
1.07833��� 0.27505�� 3.921�
0.01724 *
ctab4[5, ]�
-0.34710��� 0.07063� -4.915�
0.00796 **
---
Signif. codes:� 0
'***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2954 on 4 degrees of freedom
Multiple R-Squared: 0.8579,���� Adjusted R-squared: 0.8224
F-statistic: 24.15 on 1 and 4 DF,� p-value: 0.00796
>
> summary(lm(ctab4[4,] ~ ctab4[5,]))� # Parenting Linear Model
Call:
lm(formula = ctab4[4, ] ~ ctab4[5, ])
Residuals:
����� 1������ 2������
3������ 4������ 5������
6
�1.1855 -0.6074
-1.1390 -0.3676� 0.6544� 0.2741
Coefficients:
�����������
Estimate Std. Error t value Pr(>|t|)
(Intercept)�
-0.8168���� 0.8966� -0.911���
0.414
ctab4[5, ]���
0.2815���� 0.2302�� 1.223���
0.289
Residual standard error: 0.9631 on 4 degrees of freedom
Multiple R-Squared: 0.2721,���� Adjusted R-squared: 0.09013
F-statistic: 1.495 on 1 and 4 DF,� p-value: 0.2885
>
> summary(lm(ctab4[3,] ~ ctab4[5,]))� # SoSex Linear Model
Call:
lm(formula = ctab4[3, ] ~ ctab4[5, ])
Residuals:
����� 1������ 2������
3������ 4������ 5������
6
-1.3087�
0.5561� 1.0814� 0.6607 -0.2467 -0.7429
Coefficients:
�����������
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.04532���
0.95855� -0.047��� 0.965
ctab4[5, ]��
0.01330��� 0.24613�� 0.054���
0.960
Residual standard error: 1.03 on 4 degrees of freedom
Multiple R-Squared: 0.0007289,� Adjusted R-squared: -0.2491
F-statistic: 0.002918 on 1 and 4 DF,� p-value: 0.9595
>
> 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
-0.2233� 0.4465
-0.2233
Coefficients:
�������������
Estimate Std. Error t value Pr(>|t|)
(Intercept)���
-2.3258���� 0.8354� -2.784���
0.220
ctab4[5, 1:3]��
1.2084���� 0.3867�� 3.125���
0.197
Residual standard error: 0.5469 on 1 degrees of freedom
Multiple R-Squared: 0.9071,���� Adjusted R-squared: 0.8142
F-statistic: 9.764 on 1 and 1 DF,� p-value: 0.1972
>
> 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
�0.06852
-0.13705� 0.06852
Coefficients:
�������������
Estimate Std. Error t value Pr(>|t|)
(Intercept)����
1.2886���� 0.2564�� 5.026���
0.125
ctab4[5, 1:3]�
-0.6885���� 0.1187� -5.801���
0.109
Residual standard error: 0.1678 on 1 degrees of freedom
Multiple R-Squared: 0.9711,���� Adjusted R-squared: 0.9423
F-statistic: 33.65 on 1 and 1 DF,� p-value: 0.1087
>
> ctab4
��������������
1��������� 2��������� 3���������� 4���������� 5��������� 6
Emotn� 0.1275406
-0.1359727 -0.4056939 -0.11034841 -0.06862934�
0.3941489
Feedg�
0.5630451� 0.4597172� 0.4409716 -0.49981337 -0.95100651 -0.8320921
SoSex -1.3407316�
0.5374159� 1.0759707� 0.66858152 -0.22549639 -0.7084376
Prntg� 0.6501459
-0.8611604 -1.1112485 -0.05841974�
1.24513223� 1.1463808
cntr��
1.0000000� 2.0000000� 3.0000000�
4.00000000� 5.00000000� 6.0000000
> matplot(t(ctab4[1:4,]))