>

> 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,]))