Midterm(s) Exam: Date/time TBD
Final Exam: Date/time TBD

Office Hours:
After class

Course Outline:

(1) Introduction and Data representation
Introduction to engineering measurements, Dot plots, Stem-and-Leaf diagrams, Histograms, Box plots, Time series plots, Scatter plots

(2) Introduction to probability
Population and sample, Random variables, Mean and variance, Functions of random variables

(3) Probability distribution functions
Discrete distributions: Binomial, Poisson
Continuous distributions: Normal, Lognormal, Exponential, Weibull, Gamma Normal approximations to Binomial and Poisson distributions

(4) Error analysis
Reporting and using uncertainties, Error propagation, Random and systematic errors

(5) Engineering measurement
Sensitivity, Accuracy, Precision, Resolution, Quantization, Noise

(6) Hypothesis testing
Point estimation, z-test, t-test, χ2 test, F-test, Testing for the goodness of a fit

(7) Empirical models
Simple linear regression, Multiple regression, Least-square fitting to polynomial models

(8) Design of experiments Factorial analysis

(9) Statistical process control
X_bar and R charts, Process stability and control

Laboratory:

There are four laboratory exercises for this course. A hybrid lab model will be used this year, for which three labs are conducted remotely and one lab is conducted face-to-face on campus. Lab manuals will be posted on Canvas before each session. Laboratory report requirements, background, and a lab schedule will be made available in the third week of term. During the lab period, students will work individually or in groups as assigned. Lab reports are due one week after each lab session.

Lab 1: Coin toss (Assignment style reporting)

Lab 2: Engineering measurement (Assignment style reporting)

Lab 3: Hypothesis testing & Empirical modeling (SRYC 4270; Hands-on, Full report)

Lab 4: Design of experiments (Hands-on; Full report)

Course Objective:

This course provides an introduction to methods used in the engineering profession to collect and analyze data. At the conclusion of this course:

• Students should be able assess the presence of variability in real engineering problems and comprehend the importance of the statistical approach while making a decision.

• Students should be able to employ fundamental statistical tools that are required in statistical practice and empirical research.

• Students should gain the experience of analyzing experimental data collected in the laboratory sessions.