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: Students may receive credit for only one of MTH 2430 , MTH 2450 , and MTH 2480. This course is not available to students with credit for MTH 2410  or IND 2030 .
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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