Basic Statistics HW

Load Libraries

# remember, you might need to install packages

library(psych) # for the describe() command
library(expss) # for the cross_cases() command

Load Data

d <- read.csv(file="Data/mydata.csv", header=T)
names(d)

[1] "race_rc"    "age"        "moa_safety" "swb"        "support"   
[6] "stress"    

Univariate Plots: Histograms & Tables

table(d$race_rc) # UPDATE FOR HW!!!

      asian       black    hispanic multiracial  nativeamer       other 
        140         185         226         204           8          80 
      white 
       1278 

table(d$age)

1 between 18 and 25 2 between 26 and 35 3 between 36 and 45           4 over 45 
               1952                 115                  37                  17 

hist(d$moa_safety)

hist(d$swb)

hist(d$support)

hist(d$stress)

Univariate Normality

Check skew and kurtosis. Cuttoffs are -2 to +2; if skew or kurtosis are higher or lower than these values, I need to mention it in my writeup!!!!!!

describe(d)

           vars    n mean   sd median trimmed  mad min max range  skew kurtosis
race_rc*      1 2121 5.41 2.16   7.00    5.72 0.00 1.0 7.0   6.0 -0.84    -0.94
age*          2 2121 1.11 0.43   1.00    1.00 0.00 1.0 4.0   3.0  4.41    21.12
moa_safety    3 2121 3.21 0.65   3.25    3.28 0.74 1.0 4.0   3.0 -0.71    -0.06
swb           4 2121 4.44 1.33   4.50    4.50 1.48 1.0 7.0   6.0 -0.36    -0.48
support       5 2121 5.53 1.14   5.75    5.66 0.99 0.0 7.0   7.0 -1.10     1.36
stress        6 2121 3.06 0.60   3.10    3.06 0.59 1.3 4.6   3.3 -0.02    -0.14
             se
race_rc*   0.05
age*       0.01
moa_safety 0.01
swb        0.03
support    0.02
stress     0.01

Bivariate Plots

Crosstabs

cross_cases(d, race_rc, age)

	age
	1 between 18 and 25	2 between 26 and 35	3 between 36 and 45	4 over 45
race_rc
asian	135	4	1
black	149	29	3	4
hispanic	202	18	6
multiracial	188	12	4
nativeamer	8
other	72	5	3
white	1198	47	20	13
#Total cases	1952	115	37	17

Scatterplots

plot(d$moa_safety, d$swb,
     main="Scatterplot of Safety and Satisfaction with Life Scale",
     xlab = "Safety",
     ylab = "Satisfaction with Life Scale")

plot(d$moa_safety, d$support,
     main="Scatterplot of Safety and Multidimensional Scale of Perceived Social Support",
     xlab = "Safety",
     ylab = "Multidimensional Scale of Perceived Social Support")

plot(d$moa_safety, d$stress,
     main="Scatterplot of Safety and Perceived Stress Questionnaire",
     xlab = "Safety",
     ylab = "Perceived Stress Questionnaire")

plot(d$swb, d$support,
     main="Scatterplot of Satisfaction with Life Scale and Percieved Social Support",
     xlab = "Satisfaction with Life Scale",
     ylab = "Percieved Social Support")

plot(d$swb, d$stress,
     main="Scatterplot of Satisfaction with Life Scale and Percieved Stress Questionnaire",
     xlab = "Satisfaction with Life Scale",
     ylab = "Percieved Stress Questionnaire")

plot(d$support, d$stress,
     main="Scatterplot of Percieved Social Support and Percieved Stress Questionnaire",
     xlab = "Percieved Social Support",
     ylab = "Percieved Stress Questionnaire")

Boxplots

boxplot(data=d, swb~race_rc,
        main="Boxplot of Satisfaction with Life Scale and Race/Ethnicity",
        xlab = "Satisfaction with Life Scale",
        ylab = "Race/Ethnicity")

boxplot(data=d, swb~age,
        main="Boxplot of Satisfaction with Life Scale and Age",
        xlab = "Satisfaction with Life Scale",
        ylab = "Age")

Write-Up

We reviewed plots and descriptive statistics for our six chosen variables. Age variables had issues with skew and kurtosis: Age scores were skewed (4.41) and kurtotic (21.12). The other Race/Ethnicity, Safety, Satisfaction with Life scale, Perceived Social Support, and Percieved Stress variables had skew and kurtosis within the accepted range (-2/+2).