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library(readxl)
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library(ggpubr)
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Loading required package: ggplot2
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A4Q1 <- read_excel(“Desktop/A4Q1.xlsx”)
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View(A4Q1)
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ggscatter(A4Q1,x=“age”,y=“education”,add=“reg.line”,xlab=“Age”,ylab=“Education”)
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The relationship is linear. The relationship is positive. The relationship is weak to moderate. There are outliers. > ’’’{r}
mean(A4Q1$age)
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[1] 35.32634
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sd(A4Q1$age)
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[1] 11.45344
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median(A4Q1$age)
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[1] 35.79811
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mean(A4Q1$education)
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[1] 13.82705
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sd(A4Q1$education)
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[1] 2.595901
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median(A4Q1$education)
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[1] 14.02915
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hist(A4Q1$age,main=“Age”,breaks=20,col=“blue”,border=“white”,cex.main=1,cex.axis=1,cex.lab=1)
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hist(A4Q1$education,main=“Education”,breaks=20,col=“red”,border=“white”,cex.main=1,cex.axis=1,cex.lab=1)
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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 normally distributed. The data is symmetrical. The data has a proper bell curve.
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shapiro.test(A4Q1$age)
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Shapiro-Wilk normality test
data: A4Q1$age W = 0.99194, p-value = 0.5581
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shapiro.test(A4Q1$education)
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Shapiro-Wilk normality test
data: A4Q1$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)
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cor.test(A4Q1\(age,A4Q1\)education,method=“pearson”)
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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 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 and strong. As age increased, education increased.