Part 2. Data analysis and applied statistics. |
Lesson 2: An introduction to Statistics. One
dimensional descriptive statistics.
2.1 Statistics and scientific research.
2.2 Basic concepts: population, sample, variables.
2.3 Tabulated and graphical description.
2.4 Measures of central tendency, spread, skewness, and kurtosis.
Lesson 3. Two dimensional descriptive statistics.
3.1 Qualitative data analysis: contingency tables, graphical description
and dependency measures.
3.2 Box-plot diagram of a variable recorded by groups. Comparison of
mean and variance.
3.3 Covariance and linear correlation.
3.4 Simple linear regression model.
Lesson 4. Introduction to Statistical Inference and
Probability models.
4.1. Introduction to Statistical Inference.
4.2. Probability: basic concepts.
4.3. Random variable.
4.4. The Normal distribution. Applications.
4.5. Point estimation. The sample mean.
4.6. Calculation of the sample size.
4.7. Confidence intervals for mean and proportion
Lesson 5. Testing of Hypothesis.
5.1 Definition and classical methodology of testing: types of hypothesis,
associated errors, significance level, critical region.
5.2 p-value.
5.3 Two sample t-test
5.4 chi-squared test of independence.
5.5 Shapiro-Wilks test for normality.
5.6 Pearson correlation test. |