2016-09-22-143803.sagews

Following is the summary of MANOVA test to detect among what factors (like Age, Trimester & BMI Status) there is significant difference in the T3,T4,TSH & Hb levels.

There is some variation in distribution of T3 across Trimesters.

There is no significant variation in the distribution of other quantities between factors like Age, Trimester & BMI status

Sheet 5 Data Analysis

%r
Data5 <- read.csv('Sheet5.csv')
Allmanova5 <- manova(cbind(T3 ,T4 ,TSH,Hb) ~ Age + Trimester+ BMIStatus, data = Data5)
summary(Allmanova5)


summary.aov(Allmanova5)

Df Pillai approx F num Df den Df Pr(>F) Age 1 0.026285 0.47915 4 71 0.7509 Trimester 1 0.047529 0.88574 4 71 0.4770 BMIStatus 3 0.102791 0.64749 12 219 0.7999 Residuals 74 Response T3 : Df Sum Sq Mean Sq F value Pr(>F) Age 1 0.2474 0.24743 0.5894 0.44510 Trimester 1 1.4778 1.47780 3.5201 0.06457 . BMIStatus 3 0.2241 0.07469 0.1779 0.91104 Residuals 74 31.0664 0.41982 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Response T4 : Df Sum Sq Mean Sq F value Pr(>F) Age 1 25 25.14 0.0158 0.9002 Trimester 1 1590 1589.51 1.0005 0.3204 BMIStatus 3 3029 1009.51 0.6354 0.5945 Residuals 74 117563 1588.69 Response TSH : Df Sum Sq Mean Sq F value Pr(>F) Age 1 0.077 0.07672 0.0767 0.7826 Trimester 1 0.142 0.14216 0.1421 0.7072 BMIStatus 3 2.797 0.93226 0.9321 0.4296 Residuals 74 74.010 1.00014 Response Hb : Df Sum Sq Mean Sq F value Pr(>F) Age 1 1.725 1.72492 1.6653 0.2009 Trimester 1 0.670 0.67033 0.6471 0.4237 BMIStatus 3 2.553 0.85098 0.8215 0.4861 Residuals 74 76.651 1.03583

Box plot of T3 for Trimesters

%r
boxplot(T3~Trimester,data=Data5)

Sheet 6 Data Analysis

%r
Data6 <- read.csv('Sheet6.csv')
Allmanova6ui <- manova(cbind(T3 ,T4 ,TSH,UI) ~ Age + BMIStatus, data = Data6)
summary(Allmanova6ui)


summary.aov(Allmanova6ui)

Df Pillai approx F num Df den Df Pr(>F) Age 1 0.11061 0.96385 4 31 0.4412 BMIStatus 3 0.20603 0.60835 12 99 0.8306 Residuals 34 Response T3 : Df Sum Sq Mean Sq F value Pr(>F) Age 1 0.8000 0.80005 2.6279 0.1142 BMIStatus 3 0.3645 0.12151 0.3991 0.7545 Residuals 34 10.3511 0.30444 Response T4 : Df Sum Sq Mean Sq F value Pr(>F) Age 1 3962 3961.9 0.7305 0.3987 BMIStatus 3 14917 4972.2 0.9168 0.4432 Residuals 34 184407 5423.7 Response TSH : Df Sum Sq Mean Sq F value Pr(>F) Age 1 0.170 0.1696 0.0428 0.8373 BMIStatus 3 0.567 0.1891 0.0477 0.9859 Residuals 34 134.726 3.9625 Response UI : Df Sum Sq Mean Sq F value Pr(>F) Age 1 12774 12774.1 1.2628 0.2690 BMIStatus 3 24637 8212.2 0.8118 0.4962 Residuals 34 343930 10115.6 41 observations deleted due to missingness

Without including UI

%r
Data6 <- read.csv('Sheet6.csv')
Allmanova6 <- manova(cbind(T3 ,T4 ,TSH) ~ Age + BMIStatus, data = Data6)
summary(Allmanova6)


summary.aov(Allmanova6)

Df Pillai approx F num Df den Df Pr(>F) Age 1 0.018796 0.46612 3 73 0.7068 BMIStatus 3 0.088888 0.76335 9 225 0.6504 Residuals 75 Response T3 : Df Sum Sq Mean Sq F value Pr(>F) Age 1 1.940 1.9397 1.2815 0.2612 BMIStatus 3 3.706 1.2354 0.8162 0.4889 Residuals 75 113.520 1.5136 Response T4 : Df Sum Sq Mean Sq F value Pr(>F) Age 1 886 886.03 0.2813 0.5974 BMIStatus 3 5094 1698.12 0.5391 0.6570 Residuals 75 236234 3149.79 Response TSH : Df Sum Sq Mean Sq F value Pr(>F) Age 1 0.01 0.0075 0.0006 0.9802 BMIStatus 3 46.06 15.3542 1.2727 0.2899 Residuals 75 904.82 12.0643