• Computing & Information Technology |
• English

#### 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.

#### Textbook

Michael J. Crawley, Statistics: An Introduction using R, Wiley.

#### Course Content

content serial Description
1Sampling and probability (Random sampling- Probability calculations and combinatory) Discrete event simulation
2Distributions (Discrete- Continuous)
3Statistics and graphical display for single data (Histograms- Q–Q plots- Boxplots)
4Statistics and graphical display for grouped data (Histograms- boxplots- Strip charts)
5Generating tables and Marginal tables and relative frequency.
6Graphical display for Tables (Bar plots- Dot charts- Pie charts)
77th Week Exam + Correlation (Pearson –Spearman)
8Simple linear regression
9Residuals and fitted values.
10Prediction and confidence bands
11Comparison of variances
1212th Week Exam + One-sample T- test
13Two-sample T-test
14Wilcoxon signed-rank test + Paired sample test (T-test, Wilcoxon test)
15Revision
16Final Exam
1Sampling and probability (Random sampling- Probability calculations and combinatory) Discrete event simulation
2Distributions (Discrete- Continuous)
3Statistics and graphical display for single data (Histograms- Q–Q plots- Boxplots)
4Statistics and graphical display for grouped data (Histograms- boxplots- Strip charts)
5Generating tables and Marginal tables and relative frequency.
6Graphical display for Tables (Bar plots- Dot charts- Pie charts)
77th Week Exam + Correlation (Pearson –Spearman)
8Simple linear regression
9Residuals and fitted values.
10Prediction and confidence bands
11Comparison of variances
1212th Week Exam + One-sample T- test
13Two-sample T-test
14Wilcoxon signed-rank test + Paired sample test (T-test, Wilcoxon test)
15Revision
16Final Exam

#### Markets and Career

• Generation, transmission, distribution and utilization of electrical power for public and private sectors to secure both continuous and emergency demands.
• Electrical power feeding for civil and military marine and aviation utilities.
• Electrical works in construction engineering.