For the analysis of a continuous outcome variable, different methods are available. When two observations within the same subject are compared to each other the paired t-test can be used. However, when two groups are compared to each other the independent t-test can be used, while analysis of variance is suitable for the comparison of more than two groups. However, mostly, linear regression is used for the analysis of a continuous outcome variable. The advantage of linear regression analysis is that the method can be used in all situations, including to analyse the relationship between a continuous outcome variable and a continuous independent variable. The starting point of a linear regression analysis is a scatter plot which includes all observations. The next step is to draw a straight line through the observations for which the distance between the observations and the line is as small as possible. This regression line is characterised by two parameters which always have the same interpretation. The b0 is always the value of the outcome variable when the independent variable is 0 and the b1 always indicates that when the independent variable differs with one unit, the outcome variable differs with b1 units. In this chapter, examples are given for the analysis of a continuous outcome variable with a dichotomous, a categorical and a continuous independent variable. Furthermore, it is discussed what can be done when the continuous outcome variable is skewed, how to evaluate whether the relationship between a continuous outcome variable and a continuous independent variable is linear or not and how to adjust for confounding and how to investigate effect modification.