Homework

Note: Unless otherwise stated the problems listed below are taken from the book by Albert & Rizzo. 

Group order (using set.seed(4) sample(1:6,6) in R):  4, 6, 3, 2, 1, 5 

ASSIGNMENT #1

Chapter #1: 6, 7, 8

Chapter #2: 3, 4, 5, 6, 7, 8, 12, 13

Chapter #3: 2, 3, 4, 5, 6, 7, 8  

Due date:  Wednesday October 18;  

Group that presents solution: 4 (all groups turn homework in, but one group is in charge of providing a solution that can be posted online) 

ASSIGNMENT #2

Chapter #5: 1, 2, 5, 7

Chapter #4: 1, 4,  5,  6, 7 

Additional problem: The website https://ncsesdata.nsf.gov/datatables/gradpostdoc/2015/ contains the tables of the 2015 NSF Survey of Graduate Students and Postdoctorates in STEM. In particular, the table https://ncsesdata.nsf.gov/datatables/gradpostdoc/2015/html/GSS2015_DST_09.html corresponds to the graduate students in science, engineering and health in all institutions detailed by field from 2010-15 (use the 2014new as the data for the year 2014). Using these data and EDA tools create plots, graphs and/or summary statistics that allow you to explore the following: 

(a) How do the numbers graduate students in Science, Engineering and Health compare over the years? Does Statistics share any similarities with one or more of these groups? Are there any increasing/decreasing trends in Science, Engineering or Health? What about Statistics? 

(b) How do the numbers of graduate students in Statistics compare to the numbers/proportions of graduate students in (i) Mathematics and Applied Mathematics and (ii) Computer Science over the years? 

Due date: Wednesday October 25 at 5:20pm.  Group presenting the solution: 6

ASSIGNMENT #3

Chapter #6: 2, 3 (only a and b), 5, 7

Chapter #7: 1, 2, 3, 4, 7, 8, 9 

Due date: Monday November 6 at 5:20pm. Group presenting the solution: 3

ASSIGNMENT #4

Chapter #8: 1, 3, 4, 5

Chapter #9: 1 

Due date: Monday November 13 at 5:20pm. Group presenting the solution: 2 

ASSIGNMENT #5

The data frames below are available in the ISwR package. Make sure you install the package and type library(ISwR) before using the data.

1. Perform a two-way analysis of variance on the tb.dilute data.  Explain your model, analysis and conclusions.

2. Analyze the vitcap2 dataset in the ISwR package using analysis of covariance. Explain your model, analysis and conclusions. 

3. In the malaria dataset, analyze the risk of malaria via logistic regression with age and the log-transformed antibody level as explanatory variables. Explain your model, analysis and conclusions. 

4. Fit a logistic regression model to the graft.vs.host data predicting gvhd response. Use different transformations of the index variable. 

Due date: Wednesday November 22 at 5:20pm. Groups presenting the solution: Group 1 (problems 1, 2) and Group 5 (problems 3 and 4). 

PROJECT PRESENTATIONS MON DEC 4th: Each group has 20 mins to present including time for discussion 

(1) Mary and Wyara

(2) Fan and Jiajie 

(3) Peter and Yuanran

(4) Jennifer and Xiaotan 

PROJECT PRESENTATIONS WED DEC 6th: Each group has 20 mins to present including time for discussion 

(5) Mark and Michael

(6) Diana and Sarah

(7) Chris and Chunyi

(8) Akshar and Sean