library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A5Q1 <- read.csv("C:/Users/SHRUTI/Downloads/A5Q1(Sheet1).csv")
A5Q1
## age education
## 1 42.010815 13.202944
## 2 38.042239 12.628337
## 3 16.425751 10.302881
## 4 33.785327 16.231618
## 5 33.405561 14.037926
## 6 14.326193 11.446056
## 7 29.677576 11.817360
## 8 50.037400 17.241625
## 9 43.595877 12.996000
## 10 40.208397 15.712449
## 11 20.703037 9.240544
## 12 30.300347 15.785575
## 13 29.731887 16.203421
## 14 34.858325 6.195770
## 15 41.514464 16.017839
## 16 13.676135 9.955240
## 17 29.261494 11.575734
## 18 58.411417 18.816164
## 19 26.776763 11.475297
## 20 40.805863 14.020375
## 21 47.729118 16.504184
## 22 37.638374 14.255077
## 23 47.293863 16.000886
## 24 44.521430 10.375646
## 25 42.225728 16.907979
## 26 54.829882 19.727083
## 27 24.965057 12.342799
## 28 33.273984 12.985066
## 29 48.811017 15.107772
## 30 19.793501 12.726836
## 31 29.495737 15.693948
## 32 39.001412 11.591570
## 33 24.135549 13.143598
## 34 24.150606 14.384594
## 35 25.059156 13.015557
## 36 26.668013 13.172809
## 37 28.762200 10.361224
## 38 35.773601 13.917296
## 39 38.945754 12.805366
## 40 39.380838 15.882712
## 41 43.411073 14.798070
## 42 37.375044 15.150356
## 43 50.212304 16.147553
## 44 8.657016 12.037701
## 45 20.031573 14.810798
## 46 49.201819 11.421379
## 47 40.052170 13.346278
## 48 40.139419 17.806718
## 49 25.421144 13.972210
## 50 35.566195 16.934860
## 51 32.766138 8.371692
## 52 35.822615 10.977155
## 53 35.417189 14.701768
## 54 18.783164 10.164260
## 55 37.556385 15.393933
## 56 16.636555 12.276969
## 57 53.287841 18.748281
## 58 27.770326 14.299185
## 59 34.118509 9.898377
## 60 32.390137 13.722325
## 61 30.490076 13.009674
## 62 41.114244 14.369319
## 63 39.447594 11.734956
## 64 47.028393 17.116776
## 65 47.492355 18.333197
## 66 31.972489 9.503858
## 67 29.462561 14.194884
## 68 34.099674 15.595624
## 69 23.485583 15.171022
## 70 9.940344 13.172910
## 71 40.679617 16.264772
## 72 62.923263 16.874999
## 73 23.342844 9.515429
## 74 43.765359 13.669281
## 75 43.119202 16.140601
## 76 22.720729 11.939725
## 77 38.157875 16.184766
## 78 49.379370 17.966761
## 79 33.149064 11.586918
## 80 36.294315 16.640600
## 81 35.646306 9.406001
## 82 30.345737 13.490332
## 83 39.524352 14.191124
## 84 27.000849 14.524614
## 85 37.434439 15.759193
## 86 30.538570 16.465875
## 87 21.777119 12.375118
## 88 29.933556 12.203097
## 89 39.028024 13.850087
## 90 20.934754 13.680958
## 91 22.794940 13.988349
## 92 28.238541 14.150366
## 93 32.683961 10.046248
## 94 42.115229 17.841494
## 95 18.614248 11.872088
## 96 42.895842 10.664758
## 97 8.727262 10.141247
## 98 16.119657 14.317892
## 99 37.583849 15.999531
## 100 47.488954 14.783895
## 101 43.383481 16.210538
## 102 31.713238 14.836063
## 103 37.833086 15.268033
## 104 39.201685 14.438595
## 105 46.999009 12.034854
## 106 35.507675 14.291904
## 107 44.809940 12.936218
## 108 55.236790 15.644473
## 109 39.519016 14.978286
## 110 23.416278 15.872444
## 111 41.710276 16.298745
## 112 27.528576 14.009938
## 113 54.422819 16.918702
## 114 36.142178 11.056367
## 115 29.609052 7.652517
## 116 31.954102 9.808609
## 117 33.683755 14.114244
## 118 42.042182 19.316920
## 119 45.049456 16.502238
## 120 47.316472 15.708419
## 121 33.898088 11.799016
## 122 46.716599 13.517833
## 123 41.134342 13.314471
## 124 37.561145 17.750014
## 125 13.194830 8.556095
## 126 42.656365 16.267404
## 127 32.240635 13.149538
## 128 34.092183 13.679283
## 129 46.693579 14.797303
## 130 35.864328 14.075675
## 131 17.063319 15.203865
## 132 29.853955 11.428284
## 133 34.646683 13.017952
## 134 39.970402 15.413746
## 135 59.670739 17.567453
## 136 21.479311 11.567562
## 137 52.600402 16.216750
## 138 26.725146 8.768423
## 139 12.021259 10.938957
## 140 52.380157 16.309519
## 141 27.010017 9.947328
## 142 38.530633 12.019925
## 143 54.276011 14.259098
## 144 52.956264 18.189562
## 145 54.942208 12.304808
## 146 41.392322 14.837692
## 147 53.215994 12.361010
## 148 26.254540 15.878498
## 149 9.818089 10.018690
## 150 50.894529 13.479964
ggscatter(
A5Q1,
x = "age",
y = "education",
add = "reg.line",
xlab = "Age",
ylab = "Education"
)
# Interpretation of Scatterplot:
# The relationship is linear.
# The relationship is positive.
# There are a few minor outliers, but none are extreme.
# Age statistics
mean(A5Q1$age)
## [1] 35.32634
sd(A5Q1$age)
## [1] 11.45344
median(A5Q1$age)
## [1] 35.79811
# Education statistics
mean(A5Q1$education)
## [1] 13.82705
sd(A5Q1$education)
## [1] 2.595901
median(A5Q1$education)
## [1] 14.02915
hist(A5Q1$age,
main = "Age",
breaks = 20,
col = "lightblue",
border = "white")
# Variable 1: Age
# The variable looks normally distributed.
# The data is symmetrical.
# The data has a proper bell curve.
hist(A5Q1$education,
main = "Education",
breaks = 20,
col = "lightcoral",
border = "white")
# Variable 2: Education
# The variable looks normally distributed.
# The data is symmetrical.
# The data has a proper bell curve.
shapiro.test(A5Q1$age)
##
## Shapiro-Wilk normality test
##
## data: A5Q1$age
## W = 0.99194, p-value = 0.5581
# Age is normally distributed because p > .05.
shapiro.test(A5Q1$education)
##
## Shapiro-Wilk normality test
##
## data: A5Q1$education
## W = 0.9908, p-value = 0.4385
# Education is normally distributed because p > .05.
#Because both variables are normally distributed, we should use Pearson correlation.
cor.test(
A5Q1$age,
A5Q1$education,
method = "pearson"
)
##
## Pearson's product-moment correlation
##
## data: A5Q1$age and A5Q1$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
# Pearson Correlation Results
# A Pearson correlation was conducted to test the relationship between age (M = 35.33, SD = 11.45) and education (M = 13.83, SD = 2.60).
# 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.