1. Summarising Data (1.1 1. Displaying data dotplot, bar chart, histograms, stem-and-leaf plot, 1.2 3. Measures of centre mean, median, 1.3 4. Measures of spread standard deviation, quartiles)
2. Probability (2.1 1. Rules of probability, 2.2 2. Conditional probability, 2.3 3. Bayes theorem, 2.4 4. Independence, 2.5 5. Tree diagrams)
3. Random Variables and Special Distributions (3.1 1. Discrete and continuous random variables, 3.2 2. Expected value and variance, 3.3 3. Bernoulli random variables, 3.4 4. Uniform random variables, 3.5 5. Binomial random variables, 3.6 6. Uniform random variables, 3.7 7. Exponential random variables, 3.8 8. Normal random variables)
4. Sampling distributions and the Central Limit theorem with applications (4.1 1. Sampling distributions , 4.2 2. Central limit theorem)
5. Parameter Estimation (5.1 1. Method of moments estimation, 5.2 2. Maximum likelihood estimation)
6. Confidence Intervals (6.1 1. Confidence intervals for the population mean , 6.2 2. Confidence intervals for the population variance, 6.3 3. Confidence intervals for a population proportion, 6.4 4. Confidence intervals for a Poisson mean, 6.5 5. Two-sample confidence intervals for the difference between population means and ratio of the population variances)
7. Hypothesis Testing (7.1 1. Theory of classical fixed level hypothesis testing, 7.2 2. Standard hypothesis tests for mean, variance, population proportion, and Poisson mean., 7.3 3. P-values and their interpretation, 7.4 4. Hypothesis tests for two sample situations, i.e., the difference between two population means for independent populations and paired data, as well as ratio of two population variances.)
8. Regression (8.1 1. Simple linear regression, 8.2 2. Model fitting and checking, 8.3 3. Correlation, 8.4 4. Introduction to multiple regression)
By the end of the course, students should be able to do the following:
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SCQF Level: 8
Credits: 15