The Scatter plot shows a straight line pattern which meansthat education increases as age increases.
The relationship is linear.
The relationship is positive.
The relationship is strong.
There are no outliers
mean(A4Q1$age)
## [1] 35.32634
sd(A4Q1$age)
## [1] 11.45344
median(A4Q1$age)
## [1] 35.79811
mean(A4Q1$education)
## [1] 13.82705
sd(A4Q1$education)
## [1] 2.595901
median(A4Q1$education)
## [1] 14.02915
hist(A4Q1$age,
main = "age",
breaks = 20,
col = "lightblue",
border = "white",
cex.main = 1,
cex.axis = 1,
cex.lab = 1)
#
hist(A4Q1$education,
main = "education",
breaks = 20,
col = "lightcoral",
border = "white",
cex.main = 1,
cex.axis = 1,
cex.lab = 1)
Variable 1: age The first variable is normally distributed. The data is symmetrical. The data has a proper bell curve.
Variable 2: education The second variable is normally distributed The data is symmetrical. The data has proper bell curve.
shapiro.test(A4Q1$age)
##
## Shapiro-Wilk normality test
##
## data: A4Q1$age
## W = 0.99194, p-value = 0.5581
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 = 0.55).
Variable 2: education The second variable is normally distributed (p = 0.43).
Overall data is normal. Use Pearson Correlation
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 = 35.79, SD = 11.45) and a person’s education (M = 14.02, SD = 2.59). There was a statistically significant relationship between the two variables, r(148) = .52, p = 9.113e-12) The relationship was positive and strong. As age increased, education increased.