Module 1 - Weekly Content Review

1. Using the dataset hsb2.dta, do a detailed summary of the variable progtype (curricular program type) (In R - use the “describe” function from the psych package). Then do a tabulation of progtype using the tab command (or table command in R), both with and without the value labels. Paste the summary and the tabulations below. Which type of descriptive measure is most appropriate for this kind of data?

library(haven)
library(psych)
library(tidyverse)
hsb2<- read_dta("hsb2 (4).dta")
describe(hsb2)

table(hsb2$prog)

  1   2   3 
 45 105  50 

The most appropriate statistical measure for program type is frequency, which can be identified with the table function in R.

2. Construct a graph showing three histograms, one for each level of ses (low, medium, and high) for the write variable (standardized writing scores). In R - use filter() to create three separate datasets, one for each SES group, and then use the hist() function to make a histogram for each. Paste a picture of the graph/s below. Is it useful for comparing the writing scores by SES? Why or why not?

ses1<-filter(hsb2, ses == 1)
ses2<-filter(hsb2, ses == 2)
ses3<-filter(hsb2, ses == 3)

hist(ses1$write)

hist(ses2$write)

hist(ses3$write)

Yes, it is helpful to compare the writing scores by SES. When you filter on the students socio economic status, you notice that the histogram changes. The data suggests that students with a high socio economic status have higher standardized writing scores.