STA 303H1S辅导、讲解Studio留学生、R编程讲解、辅导R
辅导Python编程|讲解Java程序
STA 303H1S / 1002 HS -Winter 2019 Assignment # 1
“Points”
Posted: Wednesday, January 16, 2019 (minor updates made by 10am, January 17)
Due: In Crowdmark by 10pm on Monday, January 28, 2019.
Late assignments will be subjected to a penalty of 20% per day late.
Instructions:
Use R (or R Studio) to do the analysis for the following questions.
Use a benchmark significant level of 5%.
Compile your solution as a PDF document (Word, LATEXor Rmarkdown can be your base).
Presentation of solutions is very important. Your assignment should have two main sectionsSolutions
and Appendix. Include relevant plots, and quote relevant numbers from your R
output for your solutions. Unless asked otherwise, include all R codes and output in your
Appendix. Marks will be awarded for excellent presentation.
Write and submit your own work. For instance, personalized your code as much as possible,
using your first name. All plots produced must be given a title with the last 4 digits
of your student number.
Where appropriate, your answers are expected to be written in plain English.
Grading: There are 2 main questions. The grand total for this assignment is 100 marks. A general
marking scheme for each part is given below:
Per Question Part
100%: complete and correct answers
80%: answers with minor problems
60%: good answers that are unclear, contain
some mistakes, missing components
40%: poor answers with some value
20%: irrelevant answers
0: unanswered questions
Presentation and Appendix
10 points: well presented, easy to read,
proper English used, R code and extra output
in Appendix
6 points: good presentation, some R code
in main write-up.
2 points: poor presentation, handwritten,
hand-drawn diagrams, unnecessary R code
in main section.
0 point: illegible, missing R-codes/output
1. (30 marks) Consider the 3 pairs of side-by-side box plots given below, which were drawn in R.
The data for this question is in the file “juries.csv”.
1SPOCK'S OTHERS
10 20 30 40 50
Skeletal boxplots by UT##
Judge
SPOCK'S OTHERS
10 20 30 40 50
Modified boxplots by UT##
Judge
SPOCK'S OTHERS
10 20 30 40
Modified boxplots w/o initial outliers by UT##
Judge
(a) (10 marks) Recreate the side-by-side box plots and include the last four digits of your
student number in the title. For each pair of box plots, provide the single line of R code,
beginning with the R function- boxplot used to recreate it.
(b) (15 marks) For each box plot specify the values of the following - the first quartile, the
second quartile, the third quartile, the end points of the two whiskers, the extreme (outlier)
points (identified as small circles), if any, and the limits of the 1.5IQR Rule Range.
(c) (5 marks) Compare the three pairs of box plots. Which pair best represents the data and
why?
2. (60 marks) Consider the data, “bbw99.csv” based on the birth weights of 99 babies, along with
the smoking status of their mothers. Answer the questions that follow. The variables in the
dataset are:
id- an identification number from 1 to 99
bwt- baby birth weight in ounces
smoke- smoking status of mother (coded as 0, if she was a non-smoker, and 1, otherwise)
(a) (5 marks) Which variables are categorical? Name the levels of each categorical variable.
(b) (20 marks) Conduct an appropriate hypothesis test to determine whether there is a difference
in the mean birth weight between babies born to mothers who were smokers and
babies born to mothers who were nonsmokers. Include the following:
i. Side-by-side boxplots
ii. Null and Alternative Hypotheses
iii. A test statistic and it’s distribution
2iv. Test assumptions
v. Test diagnostics (checking model assumptions)
vi. P-value
vii. Results (brief discussion and conclusion)
(c) (5 marks) Name two(2) statistical methods which are equivalent to your method used in
part (b) above.
(d) (25 marks) Create a subset of the data by removing the row of observations whose ‘id’
matches the last 2 digits of your student number . For instance, this can be done in R by
shivon.subset < ?shivon.data[?1, ] if my student number ends with ‘01’.
Then redo the analyses of part (b) above with your data subset.
(e) (5 marks) Compare your results of part (b) and part (d). Do you think that the observation
removed was influential?