library(readxl)
MockJuryData <- read_excel("q2.mockjury.xlsx")
MockJuryData
## # A tibble: 114 × 12
## Seriousness exciting calm independent sincere warm sociable kind
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 8 6 9 9 8 5 9 9
## 2 8 9 5 9 3 5 9 4
## 3 5 3 4 6 3 6 4 2
## 4 3 3 6 9 8 8 9 9
## 5 9 1 1 5 1 8 9 4
## 6 9 1 5 7 5 8 9 5
## 7 4 5 6 7 6 7 7 5
## 8 4 4 9 2 9 6 2 9
## 9 5 4 8 8 7 1 1 5
## 10 2 6 8 7 5 7 9 7
## # ℹ 104 more rows
## # ℹ 4 more variables: intelligent <dbl>, strong <dbl>, sophisticated <dbl>,
## # happy <dbl>
sd(MockJuryData$exciting)
## [1] 2.315125
sd(MockJuryData$calm)
## [1] 2.390921
sd(MockJuryData$independent)
## [1] 2.494288
sd(MockJuryData$sincere)
## [1] 2.622144
sd(MockJuryData$warm)
## [1] 2.47454
sd(MockJuryData$sociable)
## [1] 2.366855
sd(MockJuryData$kind)
## [1] 2.472092
sd(MockJuryData$intelligent)
## [1] 2.818929
sd(MockJuryData$strong)
## [1] 2.327148
sd(MockJuryData$sophisticated)
## [1] 2.450519
sd(MockJuryData$happy)
## [1] 2.52872
cor(MockJuryData[2:12])
## exciting calm independent sincere warm
## exciting 1.00000000 0.23232431 0.12893165 0.09299070 0.11004167
## calm 0.23232431 1.00000000 0.07755421 0.09539183 0.10491309
## independent 0.12893165 0.07755421 1.00000000 0.17205280 0.23147890
## sincere 0.09299070 0.09539183 0.17205280 1.00000000 0.25597536
## warm 0.11004167 0.10491309 0.23147890 0.25597536 1.00000000
## sociable 0.14394846 0.06765546 0.23688237 0.10716869 0.07320244
## kind 0.18306899 0.28665593 0.13071587 0.23273282 0.59844198
## intelligent 0.03629112 0.16569424 0.25619321 0.14404676 0.38405692
## strong 0.07443421 0.11976147 0.30684230 0.23722931 0.32088399
## sophisticated 0.18156088 0.05909193 0.23611019 0.22376381 0.41301777
## happy 0.14873671 0.25486558 0.35227716 0.17680470 0.44125842
## sociable kind intelligent strong sophisticated
## exciting 0.14394846 0.18306899 0.03629112 0.07443421 0.18156088
## calm 0.06765546 0.28665593 0.16569424 0.11976147 0.05909193
## independent 0.23688237 0.13071587 0.25619321 0.30684230 0.23611019
## sincere 0.10716869 0.23273282 0.14404676 0.23722931 0.22376381
## warm 0.07320244 0.59844198 0.38405692 0.32088399 0.41301777
## sociable 1.00000000 -0.05432932 0.12408572 0.25748952 0.09166722
## kind -0.05432932 1.00000000 0.39620062 0.13555675 0.45417616
## intelligent 0.12408572 0.39620062 1.00000000 0.35999488 0.39114793
## strong 0.25748952 0.13555675 0.35999488 1.00000000 0.37314294
## sophisticated 0.09166722 0.45417616 0.39114793 0.37314294 1.00000000
## happy 0.13023305 0.38492036 0.41753727 0.40972581 0.45495424
## happy
## exciting 0.1487367
## calm 0.2548656
## independent 0.3522772
## sincere 0.1768047
## warm 0.4412584
## sociable 0.1302331
## kind 0.3849204
## intelligent 0.4175373
## strong 0.4097258
## sophisticated 0.4549542
## happy 1.0000000
MockJuryData.pc <- princomp(MockJuryData[,-1], cor = T)
summary(MockJuryData.pc, loadings = T)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.8648888 1.1392656 1.0756243 0.95764457 0.91220521
## Proportion of Variance 0.3161646 0.1179933 0.1051789 0.08337119 0.07564712
## Cumulative Proportion 0.3161646 0.4341578 0.5393367 0.62270791 0.69835503
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 0.85845354 0.83309871 0.76456831 0.72978205 0.68495750
## Proportion of Variance 0.06699477 0.06309577 0.05314225 0.04841653 0.04265153
## Cumulative Proportion 0.76534980 0.82844557 0.88158782 0.93000435 0.97265587
## Comp.11
## Standard deviation 0.54843907
## Proportion of Variance 0.02734413
## Cumulative Proportion 1.00000000
##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
## exciting 0.157 0.674 0.183 0.548 0.197 0.155 0.307
## calm 0.182 -0.122 0.627 -0.289 -0.572 -0.126
## independent 0.266 0.402 0.102 0.760 -0.305
## sincere 0.224 0.822 -0.425 0.189
## warm 0.383 -0.242 -0.156 0.121 -0.359 -0.423 0.382
## sociable 0.139 0.631 0.154 -0.537 -0.451 -0.235
## kind 0.361 -0.477 -0.311
## intelligent 0.352 -0.197 -0.325 -0.121 -0.183 0.754 0.239
## strong 0.324 0.350 -0.182 -0.165 -0.152 0.533 -0.246 0.442
## sophisticated 0.374 -0.155 0.357 -0.133 0.335 0.111 -0.582
## happy 0.395 -0.260 0.182 0.169 -0.319 -0.300
## Comp.10 Comp.11
## exciting 0.163
## calm -0.251 0.249
## independent -0.264
## sincere 0.178
## warm 0.532
## sociable
## kind -0.219 -0.691
## intelligent 0.200 0.104
## strong -0.279 -0.259
## sophisticated -0.370 0.281
## happy 0.711 -0.126
plot(1:length(MockJuryData.pc$sdev), MockJuryData.pc$sdev^2,
xlab = "Principal Component",
ylab = "Variance Explained (EigenValue Size)",
type = "b")
biplot(MockJuryData.pc, xlabs = MockJuryData$Seriousness, xlim=c(-0.25,0.25), ylim=c(-0.25,0.25),
cex = 0.8, col = c("black", "maroon"))
par(pty="s")
plot(MockJuryData.pc$scores[,1],
MockJuryData.pc$scores[,2],
ylim=range(MockJuryData.pc$scores[,1]),
xlab="PC 1", ylab="PC 2", type ='n', lwd=2)
seriousness_level <- cut(MockJuryData$Seriousness,
breaks = c(0, 3, 6, 8, 10),
labels = c("Low", "Moderately Serious", "Serious", "Very Serious"))
colors <- c("darkblue", "lightseagreen", "maroon3", "mediumpurple1")[as.numeric(seriousness_level)]
text(MockJuryData.pc$scores[,1],
MockJuryData.pc$scores[,2],
labels = seriousness_level,
cex=0.7, lwd=2,
col = colors)
library(readxl)
MockJuryMR <- read_excel("MJq2.xlsx")
MockJuryMR <- lm(Seriousness ~ exciting + calm + independent + sincere + warm +
sociable + kind + intelligent + strong + sophisticated + happy, data = MockJuryMR)
summary(MockJuryMR)
##
## Call:
## lm(formula = Seriousness ~ exciting + calm + independent + sincere +
## warm + sociable + kind + intelligent + strong + sophisticated +
## happy, data = MockJuryMR)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4788 -1.4433 -0.2642 1.4562 6.1742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.491887 0.893533 5.027 2.14e-06 ***
## exciting 0.179157 0.090266 1.985 0.0499 *
## calm 0.037693 0.091016 0.414 0.6796
## independent -0.088603 0.088009 -1.007 0.3164
## sincere -0.120838 0.079841 -1.513 0.1332
## warm -0.069092 0.108158 -0.639 0.5244
## sociable 0.005445 0.088981 0.061 0.9513
## kind -0.188807 0.114217 -1.653 0.1014
## intelligent -0.123693 0.084158 -1.470 0.1447
## strong 0.221480 0.103080 2.149 0.0340 *
## sophisticated 0.128905 0.102116 1.262 0.2097
## happy 0.121161 0.100718 1.203 0.2318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.089 on 102 degrees of freedom
## Multiple R-squared: 0.1877, Adjusted R-squared: 0.1001
## F-statistic: 2.143 on 11 and 102 DF, p-value: 0.02349