Aniruddhan Ganesaraman

PhD Student in Statistics & Operations Research, UNC Chapel Hill.

Course Goals and Learning Objectives

This course will enable you to:

  • explore data by describing patterns and departures from patterns;
  • consider the roles of sampling and experimentation in study design and implementation;
  • explore random phenomena using probability and simulation; and
  • estimate population parameters and testing hypotheses through methods of statistical inference.

Learning Outcomes:

  • Summarize, interpret, and present quantitative data in mathematical forms, such as graphs, diagrams, tables, or mathematical text.
  • Develop or compute representations of data using mathematical forms or equations as models and use statistical methods to assess their validity.
  • Make and evaluate important assumptions in the estimation, modeling, and analysis of data, and recognize the limitations of the results.
  • Apply mathematical concepts, data, procedures, and solutions to make judgments and draw conclusions.
  • Synthesize and present quantitative data to others to explain findings or to provide quantitative evidence in support of a position.

Course Topics

  1. Data analysis
  2. Correlation and regression
  3. Sampling and experimental design
  4. Basic probability (random variables, expected values, normal and binomial distributions, conditional probability)
  5. Hypothesis testing and confidence intervals for means, proportions, and regression parameters
  6. Use of spreadsheet software (Microsoft Excel)

Basic Information

   
Instructor Aniruddhan (Ani) Ganesaraman. Office Hours: Monday 16:30-17:30, Thursday 09:30-10:30 (Hanes B-26)
Teaching Assistant Leah Ghazali. Office Hours: Tuesday 12:30-13:30, Wednesday 13:00-14:00 (Hanes B-30)
Target audience This course is designed for Statistics and Analytics majors, and students from other majors who require experience with statistical skills for data analysis.
Prerequisites Math 110 (Algebra) or equivalent
Course text OpenIntro Statistics, 4th edition, by Diez, Barr, and Rundel (openintro.org/book/os)
Course format In-person lectures unless otherwise advised
Credit hours 3

Course Grading

Assessment Date %
Homeworks Every class 15%
Comprehension Checks Every class 5%
Midterm 1 24 Sep (Th) 25%
Midterm 2 05 Nov (Th) 25%
Final Exam 10 Dec (Th) 30%

Homeworks will be assigned at the end of every class at 09:15 AM and will be due the following class day at 23:59.

Comprehension checks (quizzes) will be administered via PollEverywhere during class, based on the material covered in the previous class.

93+ 90-92 87-89 83-86 80-82 77-79 73-76 70-72 67-69 60-66 Below 60
A A- B+ B B- C+ C C- D+ D F

Exam Dates

Exam Date Materials
Midterm 1 24 Sep ‘26 (Th) Practice Problem Set, Midterm 1 Solutions
Midterm 2 05 Nov ‘26 (Th)  
Final 10 Dec ‘26 (Th), 16:00-19:00  

Course Schedule & Slides

Each class’s topic below is hyperlinked to that day’s slides once they’re posted.

Class Day Date Topic(s) Reference
1 Tu 18 Aug Introduction and Case Study, Data Basics 1.1, 1.2
2 Th 20 Aug Data Basics 1.2
3 Tu 25 Aug Data Collection and Sampling Strategies, Experiments 1.3, 1.4
4 Th 27 Aug Numerical Data 1.4, 2.1
5 Tu 01 Sep Visualizing Data 2.1, 2.2
6 Th 03 Sep Correlation 8.1
7 Tu 08 Sep Linear Regression I 8.1, 8.2
8 Th 10 Sep Linear Regression II 8.2
9 Tu 15 Sep Basic Probability 3.1
10 Th 17 Sep Conditional Probability 3.1
11 Tu 22 Sep Bayes’ Theorem, Random Variables 3.2
12 Th 24 Sep Review for Midterm 1, Midterm 1 (evening)  
13 Tu 29 Sep Expectation and Variance 3.2
14 Th 01 Oct Continuous Random Variables 3.4
- Tu 06 Oct No class, Well-being day  
15 Th 08 Oct Random Variables, Density Curves 3.4, 3.5
16 Tu 13 Oct Normal Distribution 4.1
- Th 15 Oct No class, Fall break  
17 Tu 20 Oct Normal Distribution, Bernoulli Distribution 4.1, 4.2
18 Th 22 Oct Binomial Distribution 4.3
19 Tu 27 Oct Binomial Distribution, Introduction to Inference 4.3, 5.1
20 Th 29 Oct Introduction to Inference, Confidence Intervals for Proportions 5.1, 5.2
21 Tu 03 Nov Confidence Intervals for Proportions 5.2
22 Th 05 Nov Review for Midterm 2, Midterm 2 (evening)  
23 Tu 10 Nov Hypothesis Tests for Proportions 5.3
24 Th 12 Nov Hypothesis Tests for Proportions 5.3
25 Tu 17 Nov Inference for a Difference of Proportions 6.2
26 Th 20 Nov Inference for Means, t-Distribution 7.1
27 Tu 24 Nov Inference for Means, t-Distribution, Paired Data 7.1, 7.2
- Th 26 Nov No class, Thanksgiving break  
28 Tu 01 Dec Inference for Paired Data, Difference of Means 7.2, 7.3
  Th 10 Dec Final Exam Cumulative