# Australian Bureau of Statistics

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 Understanding statistics

 Module 3. Interpreting Data Lesson 1. Introduction and review 1.1 Review 1.2 Module objectives 1.3 Case study Lesson 2. Using data to support an argument Lesson 3. Questioning the data 3.1 Was the variable well defined? 3.2 How were the data produced? Lesson 4. Using summaries of data to support an argument 4.1 Estimating a population parameter from a sample statistic and estimating the size of a sample. 4.2 Using null and alternative hypotheses 4.3 What is the statistical meaning of 'significant'? 4.4 Does statistically significant mean practically significant? 4.5 How important is hypothesis testing in the production of data Lesson 5. Association between two variables 5.1 Revision and introduction 5.2 What types of relationship might exist between two quantitative variables? 5.2.1 Deterministic relationship 5.2.2 Statistical relationship 5.3 Scatter plots 5.3.1 Example of a scatterplot 5.3.2 Conventions for scatterplots 5.3.3 How can you interpret a scatter plot? 5.3.4 The correlation coefficient, r 5.3.5 The line of best fit (regression line) 5.3.6 Using the regression line to forecast 5.3.7 What happened to frequency? 5.3.8 Using a calculator for bivariate data 5.4 What about qualitative/categorical variables? Lesson 6. What about causation in observational studies? Beware the confounding variable! 6.1 Confusion of scales 6.2 Are confounding variables only found with categorical data? Lesson 7. How are studies constructed and how is causation established in observational studies? 7.1 Case Study: Vaginal cancer and synthetic oestrogen, DES 7.2 Case Study: Lung cancer and smoking Lesson 8. What relationships are possible between variables? Lesson 9. What about time? Time series plots Lesson 10. What are the steps to statistical literacy? End Notes Source Materials