Probability and Statistics
Catalog Description: Probability and statistics are areas of science that use quantitative procedures and methods that are designed to help in estimation, inference, and decision making processes. This course will provide the student with a variety of mathematical tools that help them make informed intelligent decisions when there is uncertainty in a situation.
Total Credits: 3
Contact Hours: 3 lecture hours per week
Course Coordinator: Stephen Lee Ph.D
Prereq: MATH 175: Calculus II
Textbook: None required. Recommended textbook: Probability and Statistics for Engineering and the Sciences 9th Edition by Jay L. Devore. Or Open Intro Statistics (free) See here: https://www.openintro.org/stat/textbook.pp
Lecture Notes: A combination of slides (provided on BBLearn) and oldschool white-board notes (take notes in class).
Webpage: Login via https://bblearn.uidaho.edu/webapps/login/ and choose STAT 301 on your course list.
Software: I will not use any statistical software explicitly. However, we will familiarize ourselves with R and use R code on calculating several quantities. Previous exposure to R is not required. R is an open source and free software that you can download from http://www.r-project.org/ but I recommend to use R with the graphical user interface RStudio. Computers in student computing labs are equipped with RStudio. You can download Rstudio from https://www.rstudio.com/.
Prerequisites by TopicMath 175 (Analytic Geometry & Calculus II) or equivalent.
Main Topics Covered
- Introduction to Statistical Methods
- Sample Spaces
- Discrete Random Variables
- Continuous Random Variables
- Statistical Distributions
- Hypothesis Testing
- Basic Experimental Design
- Statistical Regression and Correlation
- Describe the central tendency and variability for a dataset.
- Present statistical information graphically using histograms, box-plots, pivot charts, and other techniques.
- Conduct statistical analysis using Microsoft Excel and R.
- Calculate probabilities and evaluate risk using Monte Carlo Simulation.
- Formulate testable hypotheses and determine statistical significance for various statistical tests, including two sample t-test, paired test, AVOVA, chi-squared contingency tables, correlation, and multiple regression.
- Calibrate, validate, and use statistical models for estimation.