This course provides an introduction on Statistics. Topics of interest include the statistical analysis on statistical data, statistical measurements. Elementary probability, probability theorems, conditional probability, independent and dependent events, total probability rule and Baye's Theorem. Discrete probability distribution, probability mass function, continuous probability distribution and probability density function. Mathematical expectation: mean and variance. Special discrete distribution: Bernoulli, Binomial. Geometric and Poisson distributions. Special continuous distribution: Uniform, negative exponential and normal distribution.
Bachelor of Computer Science - 144 CRs
John E. Freund, Modern Elementary Statistics, Pearson Prentice Hall
| content serial | Description |
|---|---|
| 1 | An introduction to Statistics and statistical analysis on data observation |
| 2 | Statistical measurements |
| 3 | Elementary Probability- Probability theorems |
| 4 | Conditional probability --Independent and dependent events |
| 5 | Total probability rule – Baye’s Theorem and enumeration methods |
| 6 | Discrete probability distribution – probability mass function |
| 7 | 7th Week Examination |
| 8 | Continuous probability distribution – probability density function |
| 9 | Mathematical expectation, mean and variance |
| 10 | Special discrete distribution: Bernoulli, Binomial, Hypergeometric and Poisson distributions |
| 11 | Special continuous distribution: Uniform and exponential distribution |
| 12 | Special continuous distribution: normal distribution |
| 13 | Discrete joint probability distribution |
| 14 | Continuous joint probability distribution |
| 15 | General revision |
| 16 | Final Examination |
Start your application
