In a recent “Machine Learning Lesson of the Day“, I discussed the difference between a supervised learning model in machine learning and a regression model in statistics. In that lesson, I mentioned that a statistical regression model usually consists of a systematic component and a random component. Today’s lesson strictly concerns the systematic component. An […]
An experimenter may seek to determine the causal relationships between factors and the response, where . On first instinct, you may be tempted to conduct separate experiments, each using the completely randomized design with 1 factor. Often, however, it is possible to conduct 1 experiment with factors at the same time. This is better than […]
My last lesson introduced the matched pairs experimental design, which is a special type of the randomized blocked design. Let’s now talk about how to analyze the data from such a design. Since the experimental units are organized in pairs, the units between pairs (blocks) are not independently assigned. (The units within each pair are […]
A completely randomized design works well for a homogeneous population - one that does not have major differences between any sub-populations. However, what if a population is heterogeneous? Consider an example that commonly occurs in medical studies. An experiment seeks to determine the effectiveness of a drug on curing a disease, and 100 patients are recruited […]
The simplest experimental design is the completely randomized design with 1 factor. In this design, each experimental unit is randomly assigned to each factor level. This design is most useful for a homogeneous population (one that does not have major differences between any sub-populations). It is appealing because of its simplicity and flexibility – it can […]
In addition to choosing the number of levels for a quantitative factor in designing an experiment, the experimenter must also choose the range of the levels of the factor. If the levels are too close together, then there may not be a noticeable difference in the corresponding responses. If the levels are too far apart, […]
The goal of an experiment is to determine whether or not there is a cause-and-effect relationship between the factor and the response the strength of the causal relationship, should such a relationship exist. To answer these questions, the response variable is measured in both the control group and the experimental group. If there is a […]
Experiment: A procedure to determine the causal relationship between 2 variables – an explanatory variable and a response variable. The value of the explanatory variable is changed, and the value of the response variable is observed for each value of the explantory variable. An experiment can have 2 or more explanatory variables and 2 or […]
In his 2004 paper in Trends in Ecology and Evolution, Steven Peck argues: Simulation models can be used to mimic complex systems, but unlike nature, can be manipulated in ways that would be impossible, too costly or unethical to do in natural systems. Simulation can add to theory development and testing, can offer hypotheses about the
