Statistics With R

Title Statistics With R
Quarter Spring 2017
Instructor Dr Adam Ginensky (
Syllabus Course Description

This is a 10 week course in Statistics for individuals with some prior programming and mathematics background. This course covers statistical concepts and techniques needed for business applications. In this course statistical concepts and methods of data analysis will be taught in a practical way using R. Students will use analytical skills and statistical tools for building predictive models for decision making.   

Course Contents

  • Exploratory Data Analysis (EDA)
  • Sampling and designing experiments
  • Confidence intervals and significance test.
  • Mean, Variance, and other statistics.
  • Hypothesis Testing.
  • Linear Regression.
  • Analysis of Variance (ANOVA).

Course Objectives:
At the completion of the course, students will be able to do the following:

  • Be able to use R to implement all the methods that are taught in the class.
  • Be able to write R scripts to analyze data sets including creating necessary summary statistics.
  • Be able to run R code to create linear models (regression) and understand the output.
  • Be able to perform hypothesis tests .
  • Understand model estimation concepts and margin of error applied to analysis of a data set.
Instruction Format Coursework will have following four important components:

  • Weekly tasks
    • The course instructor will provide reading material, short videos explaining key concepts and lecture notes to be completed at home
    •  Instructor will hold regular video conferences to go over concepts where the students need help
  • Weekly Sessions with Teaching Assistants. The purpose of these labs will be:
    • Discussion on topics from the week
    • Working on data problems
    • Working on group and individual projects
    • Group projects which will require building predictive models and testing the robustness of the model with out of sample data
    • Assignment discussions
    • Quizzes and Exams

The classrooms will be wi-fi enabled and students are required to bring their laptops for the lab sessions

  • Reading Assignment and homework for the week
  • Virtual office hour with TA/Instructor via video conferencing
Assessment A letter grade A,B,C,D or F for the course will be decided based on

Projects: 40% of the final grade

  • 2 group projects and 2 individual projects

Mid Term Exam: 10% of the final grade

  • 30 minutes duration which will include both multiple choice and subjective problems

Final Exam: 15% of the final grade

  • 30 minutes duration which will include both multiple choice and subjective problems

Homework: 20% of the final grade

  • There will be 6-8 homework which will be manually graded and feedback will be provided

Quizzes: 10% of the final grade

  • There will be 4 quizzes which will have multiple choice format

Class Participation:10% of the final grade

  • Your participation will be evaluated based on lab discussion, questions/comments, replies on the discussion forum and teamwork on the group projects
TextBook There will be two texts for the class.
  • Statistics by Freedman, Pisani, and Purves 4th edition.
  • Introductory Statistics with R by Dalgaard. 2nd edition.
Pre-Requisite Ability to do basic algebraic manipulations, some basic calculus and some programming experience. In particular you must be able to download and install R and RStudio on your computer
Time Lecture Time: 8:30 pm to 10 pm EST, Wednesday
Lab Time: 8:00 pm to 9:00 pm EST Monday
Virtual Office Hour time: TBA
TA Information  
Effort Required 6-10 hours per week
Certification Participants who complete the course will receive an instructor-signed certificate with a letter grade
Computer Requirements Prior programming experience in R.