2020-04-13-In-Class-4-Trellis

Q1：Are there gender differences in the three IQ scores?

讀取資料

dta <- read.table("C:/Users/PARENTCARECHILDREN/Desktop/2020-04-13-In-Class-4-Trellis/brainsize.txt", header = T)
head(dta)

##   Sbj Gender FSIQ VIQ PIQ Weight Height MRICount
## 1   1 Female  133 132 124    118   64.5   816932
## 2   2   Male  140 150 124     NA   72.5  1001121
## 3   3   Male  139 123 150    143   73.3  1038437
## 4   4   Male  133 129 128    172   68.8   965353
## 5   5 Female  137 132 134    147   65.0   951545
## 6   6 Female   99  90 110    146   69.0   928799

畫盒狀圖，跑t-test

library(lattice)
bwplot(FSIQ ~ Gender, data=dta, xlab="Gender", ylab="Full scale IQ")

t.test(FSIQ~Gender, data=dta)

## 
##  Welch Two Sample t-test
## 
## data:  FSIQ by Gender
## t = -0.40267, df = 37.892, p-value = 0.6895
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -18.68639  12.48639
## sample estimates:
## mean in group Female   mean in group Male 
##                111.9                115.0

bwplot(VIQ ~ Gender, data=dta, xlab="Gender", ylab="Verbal IQ")

t.test(VIQ~Gender, data=dta)

## 
##  Welch Two Sample t-test
## 
## data:  VIQ by Gender
## t = -0.77262, df = 36.973, p-value = 0.4447
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -21.010922   9.410922
## sample estimates:
## mean in group Female   mean in group Male 
##               109.45               115.25

bwplot(PIQ ~ Gender, data=dta, xlab="Gender", ylab="Performance IQ")

t.test(PIQ~Gender, data=dta)

## 
##  Welch Two Sample t-test
## 
## data:  PIQ by Gender
## t = -0.1598, df = 37.815, p-value = 0.8739
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -15.72079  13.42079
## sample estimates:
## mean in group Female   mean in group Male 
##               110.45               111.60

Ans：There are no gender differences in the three IQ scores.

Q2：Is the relationship between height and weight gender dependent?

了解分布和回歸線

xyplot(Weight ~ Height, groups=Gender,auto.key=list(column=2), data=dta, xlab="Height", ylab="Weight", type=c('p', 'g', 'r'))

dta$Gender <- as.character(dta$Gender)
Male <- subset(dta, dta$Gender=="Male")
Female <- subset(dta, dta$Gender=="Female")
lm(formula =Weight ~ Height, Male)

## 
## Call:
## lm(formula = Weight ~ Height, data = Male)
## 
## Coefficients:
## (Intercept)       Height  
##      -6.279        2.420

lm(formula =Weight ~ Height, Female)

## 
## Call:
## lm(formula = Weight ~ Height, data = Female)
## 
## Coefficients:
## (Intercept)       Height  
##    -131.940        4.092

model <- lm(formula =Weight + Height ~ Gender, dta)
summary(model)

## 
## Call:
## lm(formula = Weight + Height ~ Gender, data = dta)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.817 -16.567   2.983  13.308  38.035 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  202.965      4.462  45.490  < 2e-16 ***
## GenderMale    34.852      6.483   5.376 4.74e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.95 on 36 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.4453, Adjusted R-squared:  0.4299 
## F-statistic:  28.9 on 1 and 36 DF,  p-value: 4.743e-06

Ans：身高&體種的關係，會因性別而有差異

Q3：Is the relationship between IQ and brainsize (as measured by MRIcount) gender dependent?

了解分布和回歸線

xyplot(MRICount ~ FSIQ + VIQ + PIQ, groups=Gender,auto.key=list(column=2), data=dta, xlab="IQ", ylab="MRICount", type=c('p', 'g', 'r'))

model.2 <- lm(formula =FSIQ + MRICount ~ Gender, dta)
summary(model.2)

## 
## Call:
## lm(formula = FSIQ + MRICount ~ Gender, data = dta)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -74893 -34603  -7286  19996 128676 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   862767      12502  69.008  < 2e-16 ***
## GenderMale     92204      17681   5.215 6.77e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55910 on 38 degrees of freedom
## Multiple R-squared:  0.4171, Adjusted R-squared:  0.4018 
## F-statistic: 27.19 on 1 and 38 DF,  p-value: 6.774e-06

Ans：IQ&大腦體積的關係，會因性別而有差異