STA441s18 Final Exam

This information applies only to the regular final exam, not the special deferred exam.

Time and location

Format

Homework

To study for the final, I recommend that you

1. Re-do the non-SAS parts of the homework.
   1. For each assignment, locate the corresponding lecture slides. They are pretty much in chronological order (order of time). If this is a difficult task, you are not familiar enough with the course material.
   2. Look at the lecture slides and the homework problems together. Observe how most of the homework problems are asking you to use some concept or method from the lecture. Of course sometimes I just want you to think about something, but most questions have a lesson.
   3. Re-do the problems, referring to your earlier answers
   4. If you do not get what a problem means or what it is asking you to do, this means you should find out. You are missing something, and it could be on the final exam.
2. Using SAS, do something reasonable with the final data sets described below. What's reasonable? In my opinion, more or less what you did on the SAS part of the homework. However, there is more than one "right answer." The important thing is to become familiar with the data sets, try some analyses, and understand the results. You will not bring your output to the exam. Questions will be based on my output.

Office hours

• Jerry: Wed. April 11th 11-1 in DH-4001.
• Asal: Thursday April 12th from 10-12
• Fri. April 13th 11-1 in DH-4001.
• Mon. April 16th 11-1 in DH-4001.

Quiz solutions (but no SAS code)

• Quiz 1
• Quiz 2
• Quiz 3
• Quiz 4
• Quiz 5
• Quiz 6
• Quiz 7
• Quiz 8
• Quiz 9
• Quiz 10
• Quiz 11

2016 Final exam

• Exam
• Printouts

Data sets

• The Birth Weight Data come from a sample of mothers who recently had a baby. The variables are
  • Indicator of birth weight less than 2.5 kg. This is clinically meaningful because babies who weigh less than 2,500 grams tend to have health problems.
  • Mother's age in years.
  • Mother's weight in pounds at last menstrual period.
  • Mother's race (1 = white, 2 = black, 3 = other).
  • Smoking status during pregnancy.
  • Number of previous premature labours.
  • History of hypertension.
  • Presence of uterine irritability.
  • Number of physician visits during the first trimester.
  • Baby's birth weight in grams.
  I will use the variables names given in the first line of the data file.
  
  
• The Diet Data are from a study of people trying to lose weight. Variables are
  • Identification code
  • Gender 0=F, 1=M which is not the way I do it
  • Age in years
  • Height in cm.
  • Weight in kg. before starting the diet.
  • Diet 1, 2, or 3, randomly assigned
  • Weight in kg. after 6 weeks on the diet
  These data are in an Excel spreadsheet, and column headers will automatically be the variable names.
  
  
• The Program Choice data: Incoming high school students choose their programs of study. Variables are
  • Gender: 0=Male, 1=Female
  • Socioeconomic status: 1, 2, 3
  • Math score
  • Reading score
  • Science score
  • Social studies score
  • Writing score
  • Program choice: 1=general, 2=academic, 3=vocational
  I will use the variables names given in the first line of the data file.
  
  
• The CO2 data: The CO₂ uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted. I will use the variables names given in the first line of the data file.
  
  
• The Basketball Data: Right handed basketball players take right and left-handed hook shots from the three spots on the floor (left baseline, right baseline and middle), for a total of 6 shots. Hit or miss is recorded for each shot. I will use the variables names given in the first line of the data file.

Extras

Plain language conclusions: For example, see basicmath.sas in SAS Example Three.

• tables ethnic * tongue / nocol nopercent chisq;

  First language was related to ethnic background.

• Ethnic by Tongue: Asians vs. Eastern Europeans

  Asians were less likely than Eastern Europeans to have English as their first language.

• Ethnic by Tongue: Asians vs. Other Europeans

  Asians were less likely than European not Eastern to have English as their first language.

• Ethnic by Tongue: Asians vs. Middle East

  Students whose ethnic background was judged to be Asian were less likely to have English as their first language than students whose ethnic background was judged to be Middle-Eastern or Pakistani.

• Ethnic by Tongue: Asians vs. East Indian

  Asians were less likely than East Indians to have English as their first language.

• Ethnic by Tongue: Asians vs. Other / DK

  Asians were less likely than Other/DK to have English as their first language.

• Eastern European vs. Other European

  Eastern European were less likely to have English as their first language than Europeans who were not Eastern European.

• Eastern European vs. Middle Eastern

  There was no evidence of a difference between Eastern Europeans and Middle Eastern/Pakistanis in having English as their first language.

• tables (sex ethnic tongue course) * passed / nocol nopercent chisq;

  These results are consistent with males and females being equally likely to pass the course.

  Differences between ethnic groups in passing the course were small enough to be attributed to chance.

  There was no clear evidence of a connection between first language and passing the course.

• Sex and Grade

  There was no evidence of a real difference in in the final marks of male and female students.

• Ethnic and Grade

  Differences in final marks between students from the various ethnic groups were small enough to be attributed to sampling error.

• Mother tongue and Grade

  Students whose first language was not English got higher marks on average.

• Course and Grade

  Average marks in the three courses were roughly the same.

What to say about SAS (in a job interview).

I had a course where we used SAS University Edition, so that's base SAS running in the SAS Studio environment. We read data from plain test data files using a simple form of the input statement and we used proc import to read from Excel spreadsheets. We used assignment statements and if statements to create new variables, and proc format to label the values. We used arrays and do loops in the data step. We used proc reg and proc glm for univariate and multivariate regression and analysis of variance, and we used proc logistic for regular logistic regression and multinomial logit models. We used proc mixed as well as proc glm to analyze repeated measures data when the dependent variable was assumed normal. At the end we used proc nlmixed to fit mixed logistic models when the outcome was binary and repeated measures. We used ODS to send results to proc iml for further calculations, and we also used ODS select sometimes to limit the output.

If they ask about the put statement, say "Oh, that's like a print statement for writing on the log file, but we didn't use it."

If they ask about SASgraph, say "We didn't use it. My professor said it was scary."

This document is licensed under a Creative Commons Attribution-ShareAlike 3.0 (or later) Unported License. The basketball data are protected by the Creative Commons license too.