• Introduction to R software

• An overview of statistics. Data description: measurement scales, adequate graphic display

• Summarizing data: measures of central tendency and variability • Differentiation between population and sample. How to use a statistic to estimate a population’s parameter,

• Confidence interval and its interpretation

• Hypothesis testing: how to set up Null and Alternative hypotheses, understanding Type I and Type II errors

• Comparing two population means, proportions or variances: independent data versus paired data

• Identifying relationships between two variables: categorical variables (chi-square test of independence); quantitative variables (correlation and linear regression)

• Linear regression foundations: least squares method, inferences about the parameters

• Introduction to analysis of variance (ANOVA) methods

- Teacher: Carla Maria Oliveira