MTH 2480 - Probability and Statistics

3 lecture hours 0 lab hours 3 credits

Course Description
This course provides an introduction to the laws of probability and statistical analyses. Both descriptive and inferential statistics are considered. Laws of probability, Bayes' Theorem, probability distributions, the central limit theorem, confidence intervals, hypothesis testing, and linear regression are considered. Statistical software is used.
Prereq: MTH 1110 (quarter system prereq: MA 137)
Note: Only one of MTH 2430, MTH 2450, and MTH 2480 can be used to meet a degree requirement unless approved by the program director. Students with credit for MTH 2410 or IND 2030 should not register for this course.
This course meets the following Raider Core CLO Requirement: Think Critically

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Summarize data numerically and graphically
Use software for statistical analysis
Identify the difference between a population parameter and a sample statistic
Apply the multiplication rule, permutations, and combinations to calculate the number of outcomes
Apply rules of probability to calculate the probabilities of events
Apply the law of total probability and Bayes' theorem to calculate the probabilities of events
Calculate expectation and variance of probability distributions
Apply the binomial, Poisson, normal, and exponential distributions to calculate the probabilities of events
Apply the Central Limit Theorem to calculate sampling probabilities
Construct appropriate confidence intervals for population mean and proportion
Perform hypothesis tests concerning the means and proportions of one or two populations
Construct a linear regression model and perform an inference for the regression

Prerequisites by Topic

Differentiation of algebraic and transcendental functions
Integration of algebraic and transcendental functions

Course Topics

Measures of central tendency and dispersion
Introduction to probability and the laws of probability
Conditional probability and Bayes' theorem
Discrete probability distributions: Bernoulli, binomial, and Poisson
Expectation and variance of a random variable
The Central Limit Theorem
Continuous probability distributions: normal, exponential
One-sample confidence intervals and statistical inference
One-sample hypothesis testing and statistical inference
Two-sample confidence intervals and statistical inference
Two-sample hypothesis testing and statistical inference
Correlation and simple linear regression
Analysis of variance (if time permits)

Coordinator
Dr. Won-Chul Song

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