Description

This course will help students gain an understanding of elementary probability theory and how to apply it to analyze statistical problems. It also provides an undergraduate student who is preparing for graduate study in statistical concepts to include measurements of location and dispersion, probability, probability distributions, sampling, estimation, hypothesis testing, regression, and correlation analysis.

Program

Computer Science Program.

Objectives

• 1. Use statistical methodology and tools in the problem solving process.
  2. Compute and interpret descriptive statistics using numerical and graphical techniques.
  3. Understand the basic concepts of probability, random variables, probability distribution, and joint probability distribution.
  4. Compute point estimation of parameters, explain sampling distributions, and understand the central limit theorem.
  5. Construct confidence intervals on parameters.
  6. Compute and interpret simple linear regression between two variables.
  7. Set up a least squares fit of data to a model.
  8. Use null hypothesis significance testing to test the significance of results.
  9. Use specific significance tests including T-test (one and two sample), Wilcoxon signed-rank test (one and two sample)
  10. Use software and simulation to do statistics (R) in the context of term project.