1.) Demonstrate an understanding of point estimation and its applications – beginning of the learning of statistical inference:
        * the method of moment estimator (MOME);
        * the maximum likelihood estimator (MLE);
        * order statistics and their application in deriving the MLE;
        * the difference between the MOME and the MLE;
        * unbiasedness, the minimum variance unbiased estimator (MVUE) and the Cramer-Rao Lower Bound (to identify efficient estimator, best estimator).

2.) Demonstrate an understanding of confidence intervals and their applications –continuing the learning of statistical inference:
        * pivotal quantity;
        * variable transformation techniques especially the moment generating function technique;
        * confidence interval for one population mean (including paired samples) and for one populatin variance when the population distribution is normal;
        * large sample confidence interval using the Central Limit Theorem with focus on CI for one population mean (including paired samples), and one population proportion;
        * confidence interval for a modified problem for one population mean or proportion or variance;
        * the right confidence intervals for real world problems.

3.) Demonstrate an understanding of hypothesis testing and its applications –continuing the learning of statistical inference:
        * hypothesis testing including the type I and II errors, significance level, rejection region, effect size, power of the test, and p-value;
        * hypothesis test using the pivotal quantity method for one population mean (including paired samples) and one population variance when the population distribution is normal;
       * large sample hypothesis test using the pivotal quantity method and the Central Limit Theorem for one population mean (including paired samples) or proportion;
       * hypothesis test using the likelihood ratio test method for one population mean and for one population variance when the population distribution is normal;
       * hypothesis test using the likelihood ratio test method for one population proportion;
       * asymptotic distribution of the likelihood ratio test;
       * relationship between tests derived using the likelihood ratio test method and the pivotal quantity method;
       * hypothesis test for a modified problem for one population mean or proportion or variance;
       * right hypothesis tests for real world problems.

4.) Undertake group statistical projects involving one of the following topics, including written and spoken presentation of results:
        * confidence interval and hypothesis tests for two population means, variances or proportions based on independent samples;
        * non-parametric tests for one mean, two means and several means, and the Spearman rank correlation;
        * one-way ANOVA;
        * simple linear regression and the Pearson product moment correlation;
         * multiple linear regression.