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MSIT 431: Introduction to Statistics & Data Analysis



The purpose of the course is to introduce the statistical methods that are critical in the performance analysis and selection of information systems and networks. It includes fundamental topics as well as applications: data analysis and representation; probability models; conditional probability and independence; reliability of systems and networks; binomial, Poisson, and geometric distributions; data relationships; correlation; inference with confidence; significance tests; network simulation and analysis; performance analysis of systems and networks.

REQUIRED TEXT: Moore, McCabe & Craig, “Introduction to the Practice of Statistics”, 8th Edition, Freeman, 2014.

COURSE GOALS: To provide basic understanding of probabilistic and statistical methods and the knowledge of the application of such methods to the evaluation and analysis of communication systems, information technology systems and networks, as well as application to other business models based on statistical data, including inference and hypothesis testing.


Week 1: Data; displaying distributions; describing distributions; density curves and normal distributions; the R language for statistical computing and graphics;
Week 2: Relationships; scatterplots; correlation; least-squares regression; data analysis for two-way tables;
Week 3: Producing data; random numbers; random experiments; basic combinatorics; classical probability;
Week 4: Probability space; conditional probability & independence; Bayes rule;
Week 5: Random variables; distributions; probability mass function; uniform, geometric and Poisson distributions; probability density; exponential and normal distributions;
Week 6: Midterm; expectation; variance; joint distribution; correlation; law of large numbers;
Week 7: The sampling distribution of a sample mean; sampling distributions for counts and proportions;
Week 8: Introduction to inference; estimating with confidence; tests of significance; power and inference as a decision;
Week 9: Inference for distributions;
Week 10: Inference for regression.

HOMEWORK ASSIGNMENTS:  There will be weekly assignments; midterm in Week 6 class (open book open note); one take-home final.


  • Homework: 20%
  • Midterm: 35%
  • Final: 45% 


When a student completes this course, he/she should be able to:
1. Perform basic analysis of data using statistical methods;
2. Perform simple inference of system parameters from measured data;
3. Perform simple performance analysis of network or switch models;
4. Analyze the reliability of interconnected systems;
5. Perform a significance test to verify assumptions based on measured data;
6. Make decisions based on statistical data.

Faculty Profile

Dongning Guo, Ph.D