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# FM Explanatory Variable

## 3.2 Modelling Linear Associations

### Identifying Explanatory and Response Variables

• It is important to correctly select the explanatory and response variables when using regression, or the relationship will be incorrect.
• The explanatory variable is the variable which is used to explain or predict the response variable.
• In a conventional x-y dataset, the x variable is the explanatory variable and y is the response variable.

### Fitting Least Squares Models

• Start by identifying the explanatory and response variables.
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## 2.5 Relationships between two Numerical Variables

### Guidelines to Analysing Numerical Associations

• Begin with context: what does the data represent?
• Identify the explanatory and response variables.
• Assess the form of the association: is it linear, non-linear or is there no association.
• If it is linear, assess the strength (strong, moderate or weak). Ideally, do this using the Pearson’s correlation coefficient (detailed in 2.6 Pearson’s Correlation Coefficient), however if the raw data is not available, a qualitative assessment will suffice.
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## 2.1 Response and Explanatory Variables

### Explanatory Variable

• The explanatory variable (EV) is the variable used to explain or predict another variable (the response variable).
• By convention, the explanatory variable is plotted along the x-axis of a graph, if it is numerical.

### Response Variable

• The response variable (RV) is the variable which is explained or predicted by the explanatory variable.
• By convention, the response variable is plotted along the y-axis of a graph, if it is numerical.

Note: both explanatory and response variables can be either categorical or numerical variables.

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