—
title: “HW04”
author: “me”
date: “Feb, 2020”
output:
html_document:
code_folding: hide
# number_sections: true
toc: yes
toc_depth: 2
toc_float: yes
pdf_document:
toc: yes
toc_depth: ‘2’
—
# Question 1
**After importing the dataset as Adata, check the structure. Make sure admit and rank are stored as factors, instead of numeric or integers. (Hint: Use as.factor() function or factor() function).**
Answer answer answer. Lorem ipsum dolor sit amet…
“`{r q01}
“`
Answer answer answer. Lorem ipsum dolor sit amet…
# Question 2
**Construct t-intervals for the gre and gpa data for all applicants at 0.80 level and 0.99 level. **
Answer answer answer. Lorem ipsum dolor sit amet…
# Question 3
**Repeat the same calculation for gre in Question 2 but for admitted (1) and rejected (0) separately. (You can subset them first.) Between the admitted and rejected, does the two intervals overlap at 0.80 level? And at 0.99 level?**
Answer answer answer. Lorem ipsum dolor sit amet…
# Question 4
**Make (box-) plots showing the gre distribution among applicants from different school rankings for the admitted and rejected separately. Please use ggplot for this. The x-variable should be rank, and the y-variable should be gre.**
Answer answer answer. Lorem ipsum dolor sit amet…
# Question 5
**Repeat Question 3 for gpa. Do the two groups have t-intervals overlap at 0.80 level? At 0.99 level?**
Answer answer answer. Lorem ipsum dolor sit amet…
# Question 6
**Repeat Question 4 for gpa. Make (box-) plots showing the gpa distribution among applicants from different school rankings for the admitted and rejected separately. Again, use ggplot.**
Answer answer answer. Lorem ipsum dolor sit amet…
# Question 7
**As EDA from the charts and the t-interval calculations you produced, do you observe any potential effects between gre/gpa/rank on admissions? Explain briefly. **
Answer answer answer. Lorem ipsum dolor sit amet…