’’’{r}

ggscatter(A4Q1,x=“age”,y=“education”,add=“reg.line”,xlab=“Age”,ylab=“Education”)

’’’

The relationship is linear. The relationship is positive. The relationship is weak. There are outliers.

’’’{r}

mean(A4Q1$age)

’’’

[1] 35.32634

’’’{r}

sd(A4Q1$age)

’’’ [1] 11.45344

’’’{r}

median(A4Q1$age)

’’’ [1] 35.79811

’’’{r}

mean(A4Q1$education)

’’’ [1] 13.82705

’’’{r}

sd(A4Q1$education)

’’’ [1] 2.595901

’’’{r}

median(A4Q1$education)

’’’ [1] 14.02915

’’’

’’’{r}

hist(A4Q1$age,main=“Age”,breaks=20,col=“blue”,border=“white”,cex.main=1,cex.axis=1,cex.lab=1)

’’’

’’’{r}

hist(A4Q1$education,main=“Education”,breaks=20,col=“red”,border=“white”,cex.main=1,cex.axis=1,cex.lab=1)

’’’

Variable 1: Age The first variable looks normally distributed. The data is symmetrical. The data has a proper bell curve.

Variable 2: Education The second variable looks abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.

’’’{r}

shapiro.test(A4Q1$age)

’’’

Shapiro-Wilk normality test

data: A4Q1$age W = 0.99194, p-value = 0.5581

’’’{r}

shapiro.test(A4Q1$education)

’’’

Shapiro-Wilk normality test

data: A4Q1$education W = 0.9908, p-value = 0.4385

Variable 1: Age The first variable is normally distributed (p = .56)

Variable 2: Education The second variable is normally distributed (p = .44)

’’’{r}

cor.test(A4Q1\(age,A4Q1\)education,method=“pearson”)

Pearson's product-moment correlation

data: A4Q1\(age and A4Q1\)education t = 7.4066, df = 148, p-value = 9.113e-12 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.3924728 0.6279534 sample estimates: cor 0.5200256

’’’

A Pearson correlation was conducted to test the relationship between a person’s age in years (M = 35.33, SD = 11.45) and a person’s education in years (M = 13.83, SD = 2.6) There was a statistically significant relationship between the two variables, r(148) = .52, p < .001 The relationship was positive. As age increased, education increased.