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
- Data analysis
- Correlation and regression
- Sampling and experimental design
- Basic probability (random variables, expected values, normal and binomial distributions, conditional probability)
- Hypothesis testing and confidence intervals for means, proportions, and regression parameters
- Use of spreadsheet software (Microsoft Excel)
| |
|
| 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% |
| 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 |
Syllabus |
| Midterm 1 |
24 Sep ‘26 (Th) |
Class 1-12 |
| Midterm 2 |
05 Nov ‘26 (Th) |
Class 13-22 |
| Final |
10 Dec ‘26 (Th), 16:00-19:00 |
Cumulative |
Course Schedule & Slides
| Class |
Day |
Date |
Topic(s) |
Reference |
Slides |
| 1 |
Tu |
18 Aug |
Introduction and Case Study, Data Basics |
1.1, 1.2 |
Slides |
| 2 |
Th |
20 Aug |
Data Basics |
1.2 |
Slides |
| 3 |
Tu |
25 Aug |
Data Collection and Sampling Strategies, Experiments |
1.3, 1.4 |
Slides |
| 4 |
Th |
27 Aug |
Numerical Data |
1.4, 2.1 |
Slides |
| 5 |
Tu |
01 Sep |
Numerical Data, Categorical Data |
2.1, 2.2 |
Slides |
| 6 |
Th |
03 Sep |
Correlation |
8.1 |
Slides |
| 7 |
Tu |
08 Sep |
Correlation, Linear Regression |
8.1, 8.2 |
Slides |
| 8 |
Th |
10 Sep |
Linear Regression |
8.2 |
Slides |
| 9 |
Tu |
15 Sep |
Basic Probability |
3.1 |
Slides |
| 10 |
Th |
17 Sep |
Basic Probability |
3.1 |
Slides |
| 11 |
Tu |
22 Sep |
Conditional Probability |
3.2 |
Slides |
| 12 |
Th |
24 Sep |
Review for Midterm 1, Midterm 1 (evening) |
|
|
| 13 |
Tu |
29 Sep |
Conditional Probability |
3.2 |
Slides |
| 14 |
Th |
01 Oct |
Random Variables |
3.4 |
Slides |
| - |
Tu |
06 Oct |
No class, Well-being day |
|
|
| 15 |
Th |
08 Oct |
Random Variables, Density Curves |
3.4, 3.5 |
Slides |
| 16 |
Tu |
13 Oct |
Normal Distribution |
4.1 |
Slides |
| - |
Th |
15 Oct |
No class, Fall break |
|
|
| 17 |
Tu |
20 Oct |
Normal Distribution, Bernoulli Distribution |
4.1, 4.2 |
Slides |
| 18 |
Th |
22 Oct |
Binomial Distribution |
4.3 |
Slides |
| 19 |
Tu |
27 Oct |
Binomial Distribution, Introduction to Inference |
4.3, 5.1 |
Slides |
| 20 |
Th |
29 Oct |
Introduction to Inference, Confidence Intervals for Proportions |
5.1, 5.2 |
Slides |
| 21 |
Tu |
03 Nov |
Confidence Intervals for Proportions |
5.2 |
Slides |
| 22 |
Th |
05 Nov |
Review for Midterm 2, Midterm 2 (evening) |
|
|
| 23 |
Tu |
10 Nov |
Hypothesis Tests for Proportions |
5.3 |
Slides |
| 24 |
Th |
12 Nov |
Hypothesis Tests for Proportions |
5.3 |
Slides |
| 25 |
Tu |
17 Nov |
Inference for a Difference of Proportions |
6.2 |
Slides |
| 26 |
Th |
20 Nov |
Inference for Means, t-Distribution |
7.1 |
Slides |
| 27 |
Tu |
24 Nov |
Inference for Means, t-Distribution, Paired Data |
7.1, 7.2 |
Slides |
| - |
Th |
26 Nov |
No class, Thanksgiving break |
|
|
| 28 |
Tu |
01 Dec |
Inference for Paired Data, Difference of Means |
7.2, 7.3 |
Slides |
| |
Th |
10 Dec |
Final Exam |
Cumulative |
|
