>
> 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,]))