Learning Outcomes
For each lecture the objectives are described below:
1 Description, presentation and summary data
• Identification of qualitative and quantitative variables and different types of data
• Presentation and summary data in an appropriate way
• Calculation and interpretation of graphs
• Understand the characteristics of the normal distribution of their difference from the asymmetric distributions
2 Measures of central location and dispersion
• Calculation of measures of central location and dispersion
• Understand the use of appropriate measures of central location and dispersion for summarizing data with normal and skewed distribution
• Description and interpretation of results of measures of central location and dispersion
3 Populations and samples
• Understand the differences in measurements between the population and sample
• Different types of selecting a random sample from a population
• Understand the types of random sampling and their application
• Assessment of the accuracy and repeatability of sampling
• Identify the disadvantages of non-random samples
4 Normal distribution
• Identification of the properties of a normal distribution
• Transformation of a normal distribution to a standard normal distribution
• Calculation of various probabilities based on standard distribution
• Find critical values from the table of normal distribution
• Understand the properties of the sampling distribution
• Estimate the difference between the standard deviation and standard error of the mean
5 Hypothesis testing
• Formulate the null and alternative hypothesis
• Understand the concept of P-value, the level of significance and the corresponding critical value
• Testing and assessing the significant difference
• Separation between one-sided and two-sided tests
• Understand type I and II errors and when the power of a study increases
6 Parametric tests of one sample
• Understand application Z and t test
• Formulate the null and alternative hypothesis in the case of a control sample
• Calculation of the Z and t test
• Read and find critical values of the t distribution table
• Interpretation of the results of the test
• Calculation of confidence intervals
7 Parametric tests of two samples
• Understand the difference between independent and dependent samples
• Formulate the null and alternative hypothesis in hypothesis testing between two independent or dependent samples
• Calculation of the t test for two independent or dependent samples
• Interpretation of the results of the tests
• Calculation of confidence intervals
8 Analysis of variance
• Formulate the null and alternative hypothesis in hypothesis testing between three or more independent samples
• Understand the problem of multiple comparisons and how to correct it
• Understand the calculation of the F-ratio
• Read and find critical values from F-distribution table
• Configuring the analysis of variance table
• Procedures for correcting multiple comparisons
9 Correlation between continuous variables
• Understand the importance of the correlation coefficient
• Assessment of the application of different correlation coefficients depending on the types of data
• Calculation of the Pearson correlation coefficient
• Calculation of non-parametric Pearson correlation coefficient
10 Association of qualitative variables
• Understand the application of the chi-square criterion
• Calculation of the goodness of fit test with equal and proportionate number of expected frequencies
• Calculation of the chi-square test for independency and interpretation of results
• Configuring the contingency table
• Calculation of the chi-square test for fourfold (2x2)
• Calculation of the Z test for comparing two proportions in a fourfold table
• Implementation of the Mc Nemar test for dependent samples
11 Non-parametric tests
• Understand the utility of non-parametric tests
• Recognition of the parametric test applied to the relevant comparison
• Application of the Mann-Whitney test to compare two independent samples
• Implementation of the Wilcoxon test for comparing two dependent samples
• Implementation of the Kruskal-Wallis test for comparison of three independent samples and above
• Assessment of the advantages and disadvantages of non-parametric tests
12 Diagnostic testing
• Calculation and interpretation of sensitivity, specificity, predictive value of positive and negative results
• Understand the restrictions on the use of positive and negative predictive values
• Interpretation of ROC curve
13 Survival analysis
• Decision on data in which the survival analysis is applied
• Understand the concept of censored data
• Calculation and interpretation of life tables
• Interpretation of Kaplan-Meier curves
• A comparison of two survival curves by Log rank test
• Assessment of the assumptions that must be met to implement the Log-rank test
Course Content (Syllabus)
Lecture
1 Description, presentation and summary data
2 Measures of central location and dispersion
3 Populations and samples
4 Normal distribution
5 Hypothesis testing
6 Parametric tests of one sample
7 Parametric tests of two samples
8 Analysis of variance
9 Association between two continuous variables
10 Association of qualitative variables
11 Non parametric tests
12 Diagnostic testing
13 Survival analysis