Statistical modelling includes the use of simple and multi­ple linear regression (Chapters 27–29), logistic regression (Chapter 30), Poisson regression (Chapter 31) and some methods that deal with survival data (Chapter…

Why Do We Sample? In statistics, a population represents the entire group of individuals in whom we are interested. Generally it is costly and labour-intensive to study the entire population…

What is Linear Regression? To investigate the relationship between two numerical variables, x and y, we measure the values of x and y on each of the n individuals in…

Why Transform? The observations in our investigation may not comply with the requirements of the intended statistical analysis (Chapter 35). A variable may not be Normally distributed, a distributional requirement…

Why Do We Do It? Given a large number of demographic or clinical features of an individual, we may want to predict whether that individual is likely to experience an…

Introduction An essential facet of statistics is the ability to summarize the important features of the analysis. We must know what to include and how to display our results in…

Making a Decision Most hypothesis tests in medical statistics compare groups of people who are exposed to a variety of experiences. We may, for example, be interested in comparing the…

Whichever type of statistical model we choose, we have to make decisions about which explanatory variables to include in the model and the most appropriate way in which they should…

Some Words of Comfort Do not worry if you find the theory underlying probability distributions complex. Our experience demonstrates that you want to know only when and how to use…

Straus et al. 1 describe evidence-based medicine (EBM) as ‘the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients’. To practice…